AWS.Tools.MachineLearning.XML
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<doc> <assembly> <name>AWS.Tools.MachineLearning</name> </assembly> <members> <member name="T:Amazon.PowerShell.Cmdlets.ML.AddMLResourceTagCmdlet"> <summary> Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, <code>AddTags</code> updates the tag's value. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.AddMLResourceTagCmdlet.ResourceId"> <summary> <para> <para>The ID of the ML object to tag. For example, <code>exampleModelId</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.AddMLResourceTagCmdlet.ResourceType"> <summary> <para> <para>The type of the ML object to tag. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.AddMLResourceTagCmdlet.Tag"> <summary> <para> <para>The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.AddMLResourceTagCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionCmdlet"> <summary> Returns a <code>BatchPrediction</code> that includes detailed metadata, status, and data file information for a <code>Batch Prediction</code> request. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionCmdlet.BatchPredictionId"> <summary> <para> <para>An ID assigned to the <code>BatchPrediction</code> at creation.</para> </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet"> <summary> Returns a list of <code>BatchPrediction</code> operations that match the search criteria in the request.<br/><br/>This operation automatically pages all available results to the pipeline - parameters related to iteration are only needed if you want to manually control the paginated output. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.EQ"> <summary> <para> <para>The equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that exactly match the value specified with <code>EQ</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.FilterVariable"> <summary> <para> <para>Use one of the following variables to filter a list of <code>BatchPrediction</code>:</para><ul><li><code>CreatedAt</code> - Sets the search criteria to the <code>BatchPrediction</code> creation date.</li><li><code>Status</code> - Sets the search criteria to the <code>BatchPrediction</code> status.</li><li><code>Name</code> - Sets the search criteria to the contents of the <code>BatchPrediction</code><b></b><code>Name</code>.</li><li><code>IAMUser</code> - Sets the search criteria to the user account that invoked the <code>BatchPrediction</code> creation.</li><li><code>MLModelId</code> - Sets the search criteria to the <code>MLModel</code> used in the <code>BatchPrediction</code>.</li><li><code>DataSourceId</code> - Sets the search criteria to the <code>DataSource</code> used in the <code>BatchPrediction</code>.</li><li><code>DataURI</code> - Sets the search criteria to the data file(s) used in the <code>BatchPrediction</code>. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.</li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.GE"> <summary> <para> <para>The greater than or equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are greater than or equal to the value specified with <code>GE</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.GT"> <summary> <para> <para>The greater than operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are greater than the value specified with <code>GT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.LE"> <summary> <para> <para>The less than or equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are less than or equal to the value specified with <code>LE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.LT"> <summary> <para> <para>The less than operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values that are less than the value specified with <code>LT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.NE"> <summary> <para> <para>The not equal to operator. The <code>BatchPrediction</code> results will have <code>FilterVariable</code> values not equal to the value specified with <code>NE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.Prefix"> <summary> <para> <para>A string that is found at the beginning of a variable, such as <code>Name</code> or <code>Id</code>.</para><para>For example, a <code>Batch Prediction</code> operation could have the <code>Name</code><code>2014-09-09-HolidayGiftMailer</code>. To search for this <code>BatchPrediction</code>, select <code>Name</code> for the <code>FilterVariable</code> and any of the following strings for the <code>Prefix</code>: </para><ul><li><para>2014-09</para></li><li><para>2014-09-09</para></li><li><para>2014-09-09-Holiday</para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.SortOrder"> <summary> <para> <para>A two-value parameter that determines the sequence of the resulting list of <code>MLModel</code>s.</para><ul><li><code>asc</code> - Arranges the list in ascending order (A-Z, 0-9).</li><li><code>dsc</code> - Arranges the list in descending order (Z-A, 9-0).</li></ul><para>Results are sorted by <code>FilterVariable</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.Limit"> <summary> <para> <para>The number of pages of information to include in the result. The range of acceptable values is <code>1</code> through <code>100</code>. The default value is <code>100</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLBatchPredictionListCmdlet.NextToken"> <summary> <para> <para>An ID of the page in the paginated results.</para> </para> <para> <br/><b>Note:</b> This parameter is only used if you are manually controlling output pagination of the service API call. <br/>In order to manually control output pagination, assign $null, for the first call, and the value of $AWSHistory.LastServiceResponse.NextToken, for subsequent calls, to this parameter. </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceCmdlet"> <summary> Returns a <code>DataSource</code> that includes metadata and data file information, as well as the current status of the <code>DataSource</code>. <para><code>GetDataSource</code> provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceCmdlet.DataSourceId"> <summary> <para> <para>The ID assigned to the <code>DataSource</code> at creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceCmdlet.VerboseResponse"> <summary> <para> <para>Specifies whether the <code>GetDataSource</code> operation should return <code>DataSourceSchema</code>.</para><para>If true, <code>DataSourceSchema</code> is returned.</para><para>If false, <code>DataSourceSchema</code> is not returned.</para> </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet"> <summary> Returns a list of <code>DataSource</code> that match the search criteria in the request.<br/><br/>This operation automatically pages all available results to the pipeline - parameters related to iteration are only needed if you want to manually control the paginated output. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.EQ"> <summary> <para> <para>The equal to operator. The <code>DataSource</code> results will have <code>FilterVariable</code> values that exactly match the value specified with <code>EQ</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.FilterVariable"> <summary> <para> <para>Use one of the following variables to filter a list of <code>DataSource</code>:</para><ul><li><code>CreatedAt</code> - Sets the search criteria to <code>DataSource</code> creation dates.</li><li><code>Status</code> - Sets the search criteria to <code>DataSource</code> statuses.</li><li><code>Name</code> - Sets the search criteria to the contents of <code>DataSource</code><b></b><code>Name</code>.</li><li><code>DataUri</code> - Sets the search criteria to the URI of data files used to create the <code>DataSource</code>. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.</li><li><code>IAMUser</code> - Sets the search criteria to the user account that invoked the <code>DataSource</code> creation.</li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.GE"> <summary> <para> <para>The greater than or equal to operator. The <code>DataSource</code> results will have <code>FilterVariable</code> values that are greater than or equal to the value specified with <code>GE</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.GT"> <summary> <para> <para>The greater than operator. The <code>DataSource</code> results will have <code>FilterVariable</code> values that are greater than the value specified with <code>GT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.LE"> <summary> <para> <para>The less than or equal to operator. The <code>DataSource</code> results will have <code>FilterVariable</code> values that are less than or equal to the value specified with <code>LE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.LT"> <summary> <para> <para>The less than operator. The <code>DataSource</code> results will have <code>FilterVariable</code> values that are less than the value specified with <code>LT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.NE"> <summary> <para> <para>The not equal to operator. The <code>DataSource</code> results will have <code>FilterVariable</code> values not equal to the value specified with <code>NE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.Prefix"> <summary> <para> <para>A string that is found at the beginning of a variable, such as <code>Name</code> or <code>Id</code>.</para><para>For example, a <code>DataSource</code> could have the <code>Name</code><code>2014-09-09-HolidayGiftMailer</code>. To search for this <code>DataSource</code>, select <code>Name</code> for the <code>FilterVariable</code> and any of the following strings for the <code>Prefix</code>: </para><ul><li><para>2014-09</para></li><li><para>2014-09-09</para></li><li><para>2014-09-09-Holiday</para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.SortOrder"> <summary> <para> <para>A two-value parameter that determines the sequence of the resulting list of <code>DataSource</code>.</para><ul><li><code>asc</code> - Arranges the list in ascending order (A-Z, 0-9).</li><li><code>dsc</code> - Arranges the list in descending order (Z-A, 9-0).</li></ul><para>Results are sorted by <code>FilterVariable</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.Limit"> <summary> <para> <para> The maximum number of <code>DataSource</code> to include in the result.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLDataSourceListCmdlet.NextToken"> <summary> <para> <para>The ID of the page in the paginated results.</para> </para> <para> <br/><b>Note:</b> This parameter is only used if you are manually controlling output pagination of the service API call. <br/>In order to manually control output pagination, assign $null, for the first call, and the value of $AWSHistory.LastServiceResponse.NextToken, for subsequent calls, to this parameter. </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationCmdlet"> <summary> Returns an <code>Evaluation</code> that includes metadata as well as the current status of the <code>Evaluation</code>. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationCmdlet.EvaluationId"> <summary> <para> <para>The ID of the <code>Evaluation</code> to retrieve. The evaluation of each <code>MLModel</code> is recorded and cataloged. The ID provides the means to access the information. </para> </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet"> <summary> Returns a list of <code>DescribeEvaluations</code> that match the search criteria in the request.<br/><br/>This operation automatically pages all available results to the pipeline - parameters related to iteration are only needed if you want to manually control the paginated output. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.EQ"> <summary> <para> <para>The equal to operator. The <code>Evaluation</code> results will have <code>FilterVariable</code> values that exactly match the value specified with <code>EQ</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.FilterVariable"> <summary> <para> <para>Use one of the following variable to filter a list of <code>Evaluation</code> objects:</para><ul><li><code>CreatedAt</code> - Sets the search criteria to the <code>Evaluation</code> creation date.</li><li><code>Status</code> - Sets the search criteria to the <code>Evaluation</code> status.</li><li><code>Name</code> - Sets the search criteria to the contents of <code>Evaluation</code><b></b><code>Name</code>.</li><li><code>IAMUser</code> - Sets the search criteria to the user account that invoked an <code>Evaluation</code>.</li><li><code>MLModelId</code> - Sets the search criteria to the <code>MLModel</code> that was evaluated.</li><li><code>DataSourceId</code> - Sets the search criteria to the <code>DataSource</code> used in <code>Evaluation</code>.</li><li><code>DataUri</code> - Sets the search criteria to the data file(s) used in <code>Evaluation</code>. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.</li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.GE"> <summary> <para> <para>The greater than or equal to operator. The <code>Evaluation</code> results will have <code>FilterVariable</code> values that are greater than or equal to the value specified with <code>GE</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.GT"> <summary> <para> <para>The greater than operator. The <code>Evaluation</code> results will have <code>FilterVariable</code> values that are greater than the value specified with <code>GT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.LE"> <summary> <para> <para>The less than or equal to operator. The <code>Evaluation</code> results will have <code>FilterVariable</code> values that are less than or equal to the value specified with <code>LE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.LT"> <summary> <para> <para>The less than operator. The <code>Evaluation</code> results will have <code>FilterVariable</code> values that are less than the value specified with <code>LT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.NE"> <summary> <para> <para>The not equal to operator. The <code>Evaluation</code> results will have <code>FilterVariable</code> values not equal to the value specified with <code>NE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.Prefix"> <summary> <para> <para>A string that is found at the beginning of a variable, such as <code>Name</code> or <code>Id</code>.</para><para>For example, an <code>Evaluation</code> could have the <code>Name</code><code>2014-09-09-HolidayGiftMailer</code>. To search for this <code>Evaluation</code>, select <code>Name</code> for the <code>FilterVariable</code> and any of the following strings for the <code>Prefix</code>: </para><ul><li><para>2014-09</para></li><li><para>2014-09-09</para></li><li><para>2014-09-09-Holiday</para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.SortOrder"> <summary> <para> <para>A two-value parameter that determines the sequence of the resulting list of <code>Evaluation</code>.</para><ul><li><code>asc</code> - Arranges the list in ascending order (A-Z, 0-9).</li><li><code>dsc</code> - Arranges the list in descending order (Z-A, 9-0).</li></ul><para>Results are sorted by <code>FilterVariable</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.Limit"> <summary> <para> <para> The maximum number of <code>Evaluation</code> to include in the result.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLEvaluationListCmdlet.NextToken"> <summary> <para> <para>The ID of the page in the paginated results.</para> </para> <para> <br/><b>Note:</b> This parameter is only used if you are manually controlling output pagination of the service API call. <br/>In order to manually control output pagination, assign $null, for the first call, and the value of $AWSHistory.LastServiceResponse.NextToken, for subsequent calls, to this parameter. </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLModelCmdlet"> <summary> Returns an <code>MLModel</code> that includes detailed metadata, data source information, and the current status of the <code>MLModel</code>. <para><code>GetMLModel</code> provides results in normal or verbose format. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelCmdlet.MLModelId"> <summary> <para> <para>The ID assigned to the <code>MLModel</code> at creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelCmdlet.VerboseResponse"> <summary> <para> <para>Specifies whether the <code>GetMLModel</code> operation should return <code>Recipe</code>.</para><para>If true, <code>Recipe</code> is returned.</para><para>If false, <code>Recipe</code> is not returned.</para> </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet"> <summary> Returns a list of <code>MLModel</code> that match the search criteria in the request.<br/><br/>This operation automatically pages all available results to the pipeline - parameters related to iteration are only needed if you want to manually control the paginated output. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.EQ"> <summary> <para> <para>The equal to operator. The <code>MLModel</code> results will have <code>FilterVariable</code> values that exactly match the value specified with <code>EQ</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.FilterVariable"> <summary> <para> <para>Use one of the following variables to filter a list of <code>MLModel</code>:</para><ul><li><code>CreatedAt</code> - Sets the search criteria to <code>MLModel</code> creation date.</li><li><code>Status</code> - Sets the search criteria to <code>MLModel</code> status.</li><li><code>Name</code> - Sets the search criteria to the contents of <code>MLModel</code><b></b><code>Name</code>.</li><li><code>IAMUser</code> - Sets the search criteria to the user account that invoked the <code>MLModel</code> creation.</li><li><code>TrainingDataSourceId</code> - Sets the search criteria to the <code>DataSource</code> used to train one or more <code>MLModel</code>.</li><li><code>RealtimeEndpointStatus</code> - Sets the search criteria to the <code>MLModel</code> real-time endpoint status.</li><li><code>MLModelType</code> - Sets the search criteria to <code>MLModel</code> type: binary, regression, or multi-class.</li><li><code>Algorithm</code> - Sets the search criteria to the algorithm that the <code>MLModel</code> uses.</li><li><code>TrainingDataURI</code> - Sets the search criteria to the data file(s) used in training a <code>MLModel</code>. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.</li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.GE"> <summary> <para> <para>The greater than or equal to operator. The <code>MLModel</code> results will have <code>FilterVariable</code> values that are greater than or equal to the value specified with <code>GE</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.GT"> <summary> <para> <para>The greater than operator. The <code>MLModel</code> results will have <code>FilterVariable</code> values that are greater than the value specified with <code>GT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.LE"> <summary> <para> <para>The less than or equal to operator. The <code>MLModel</code> results will have <code>FilterVariable</code> values that are less than or equal to the value specified with <code>LE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.LT"> <summary> <para> <para>The less than operator. The <code>MLModel</code> results will have <code>FilterVariable</code> values that are less than the value specified with <code>LT</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.NE"> <summary> <para> <para>The not equal to operator. The <code>MLModel</code> results will have <code>FilterVariable</code> values not equal to the value specified with <code>NE</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.Prefix"> <summary> <para> <para>A string that is found at the beginning of a variable, such as <code>Name</code> or <code>Id</code>.</para><para>For example, an <code>MLModel</code> could have the <code>Name</code><code>2014-09-09-HolidayGiftMailer</code>. To search for this <code>MLModel</code>, select <code>Name</code> for the <code>FilterVariable</code> and any of the following strings for the <code>Prefix</code>: </para><ul><li><para>2014-09</para></li><li><para>2014-09-09</para></li><li><para>2014-09-09-Holiday</para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.SortOrder"> <summary> <para> <para>A two-value parameter that determines the sequence of the resulting list of <code>MLModel</code>.</para><ul><li><code>asc</code> - Arranges the list in ascending order (A-Z, 0-9).</li><li><code>dsc</code> - Arranges the list in descending order (Z-A, 9-0).</li></ul><para>Results are sorted by <code>FilterVariable</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.Limit"> <summary> <para> <para>The number of pages of information to include in the result. The range of acceptable values is <code>1</code> through <code>100</code>. The default value is <code>100</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLModelListCmdlet.NextToken"> <summary> <para> <para>The ID of the page in the paginated results.</para> </para> <para> <br/><b>Note:</b> This parameter is only used if you are manually controlling output pagination of the service API call. <br/>In order to manually control output pagination, assign $null, for the first call, and the value of $AWSHistory.LastServiceResponse.NextToken, for subsequent calls, to this parameter. </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLPredictionCmdlet"> <summary> Generates a prediction for the observation using the specified <code>ML Model</code>. <note><title>Note</title><para> Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested. </para></note> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLPredictionCmdlet.MLModelId"> <summary> <para> <para>A unique identifier of the <code>MLModel</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLPredictionCmdlet.PredictEndpoint"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLPredictionCmdlet.Record"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.GetMLResourceTagCmdlet"> <summary> Describes one or more of the tags for your Amazon ML object. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLResourceTagCmdlet.ResourceId"> <summary> <para> <para>The ID of the ML object. For example, <code>exampleModelId</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.GetMLResourceTagCmdlet.ResourceType"> <summary> <para> <para>The type of the ML object.</para> </para> </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet"> <summary> Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a <code>DataSource</code>. This operation creates a new <code>BatchPrediction</code>, and uses an <code>MLModel</code> and the data files referenced by the <code>DataSource</code> as information sources. <para><code>CreateBatchPrediction</code> is an asynchronous operation. In response to <code>CreateBatchPrediction</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>BatchPrediction</code> status to <code>PENDING</code>. After the <code>BatchPrediction</code> completes, Amazon ML sets the status to <code>COMPLETED</code>. </para><para> You can poll for status updates by using the <a>GetBatchPrediction</a> operation and checking the <code>Status</code> parameter of the result. After the <code>COMPLETED</code> status appears, the results are available in the location specified by the <code>OutputUri</code> parameter. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet.BatchPredictionDataSourceId"> <summary> <para> <para>The ID of the <code>DataSource</code> that points to the group of observations to predict.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet.BatchPredictionId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet.BatchPredictionName"> <summary> <para> <para>A user-supplied name or description of the <code>BatchPrediction</code>. <code>BatchPredictionName</code> can only use the UTF-8 character set.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet.MLModelId"> <summary> <para> <para>The ID of the <code>MLModel</code> that will generate predictions for the group of observations. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet.OutputUri"> <summary> <para> <para>The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the <code>s3 key</code> portion of the <code>outputURI</code> field: ':', '//', '/./', '/../'.</para><para>Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the <a href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLBatchPredictionCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet"> <summary> Creates a <code>DataSource</code> object from an <a href="http://aws.amazon.com/rds/"> Amazon Relational Database Service</a> (Amazon RDS). A <code>DataSource</code> references data that can be used to perform <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations. <para><code>CreateDataSourceFromRDS</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRDS</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can be used only to perform <code>>CreateMLModel</code>>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations. </para><para> If Amazon ML cannot accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.ComputeStatistic"> <summary> <para> <para>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code></code>DataSource<code></code> needs to be used for <code>MLModel</code> training. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.DatabaseInformation_DatabaseName"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_DataRearrangement"> <summary> <para> <para>A JSON string that represents the splitting and rearrangement processing to be applied to a <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data is used to create the <code>Datasource</code>.</para><para>There are multiple parameters that control what data is used to create a datasource:</para><ul><li><para><b><code>percentBegin</code></b></para><para>Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when creating the datasource.</para></li><li><para><b><code>percentEnd</code></b></para><para>Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when creating the datasource.</para></li><li><para><b><code>complement</code></b></para><para>The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code> parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along with the <code>complement</code> parameter.</para><para>For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code></para></li><li><para><b><code>strategy</code></b></para><para>To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.</para><para>The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for the datasource, in the order that the records appear in the input data.</para><para>The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and evaluation datasources:</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code></para><para>To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.</para><para>The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and evaluation datasources:</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code></para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_DataSchema"> <summary> <para> <para>A JSON string that represents the schema for an Amazon RDS <code>DataSource</code>. The <code>DataSchema</code> defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.</para><para>A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code></para><para>Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to define your <code>DataSchema</code>.</para><para>{ "version": "1.0",</para><para> "recordAnnotationFieldName": "F1",</para><para> "recordWeightFieldName": "F2",</para><para> "targetFieldName": "F3",</para><para> "dataFormat": "CSV",</para><para> "dataFileContainsHeader": true,</para><para> "attributes": [</para><para> { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],</para><para> "excludedVariableNames": [ "F6" ] } </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_DataSchemaUri"> <summary> <para> <para>The Amazon S3 location of the <code>DataSchema</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.DataSourceId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>DataSource</code>. Typically, an Amazon Resource Number (ARN) becomes the ID for a <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.DataSourceName"> <summary> <para> <para>A user-supplied name or description of the <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.DatabaseInformation_InstanceIdentifier"> <summary> <para> <para>The ID of an RDS DB instance.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.DatabaseCredentials_Password"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_ResourceRole"> <summary> <para> <para>The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for data pipelines.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RoleARN"> <summary> <para> <para>The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the <code>SelectSqlQuery</code> query from Amazon RDS to Amazon S3.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_S3StagingLocation"> <summary> <para> <para>The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using <code>SelectSqlQuery</code> is stored in this location.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_SecurityGroupId"> <summary> <para> <para>The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_SelectSqlQuery"> <summary> <para> <para>The query that is used to retrieve the observation data for the <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_ServiceRole"> <summary> <para> <para>The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see <a href="http://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/dp-iam-roles.html">Role templates</a> for data pipelines.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.RDSData_SubnetId"> <summary> <para> <para>The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.DatabaseCredentials_Username"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRDSCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet"> <summary> Creates a <code>DataSource</code> from a database hosted on an Amazon Redshift cluster. A <code>DataSource</code> references data that can be used to perform either <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations. <para><code>CreateDataSourceFromRedshift</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRedshift</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in <code>COMPLETED</code> or <code>PENDING</code> states can be used to perform only <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations. </para><para> If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response. </para><para> The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a <code>SelectSqlQuery</code> query. Amazon ML executes an <code>Unload</code> command in Amazon Redshift to transfer the result set of the <code>SelectSqlQuery</code> query to <code>S3StagingLocation</code>. </para><para> After the <code>DataSource</code> has been created, it's ready for use in evaluations and batch predictions. If you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also requires a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. </para><para> You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call <code>GetDataSource</code> for an existing datasource and copy the values to a <code>CreateDataSource</code> call. Change the settings that you want to change and make sure that all required fields have the appropriate values. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DatabaseInformation_ClusterIdentifier"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.ComputeStatistic"> <summary> <para> <para>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code>DataSource</code> needs to be used for <code>MLModel</code> training.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DatabaseInformation_DatabaseName"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSpec_DataRearrangement"> <summary> <para> <para>A JSON string that represents the splitting and rearrangement processing to be applied to a <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data is used to create the <code>Datasource</code>.</para><para>There are multiple parameters that control what data is used to create a datasource:</para><ul><li><para><b><code>percentBegin</code></b></para><para>Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when creating the datasource.</para></li><li><para><b><code>percentEnd</code></b></para><para>Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when creating the datasource.</para></li><li><para><b><code>complement</code></b></para><para>The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code> parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along with the <code>complement</code> parameter.</para><para>For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code></para></li><li><para><b><code>strategy</code></b></para><para>To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.</para><para>The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for the datasource, in the order that the records appear in the input data.</para><para>The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and evaluation datasources:</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code></para><para>To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.</para><para>The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and evaluation datasources:</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code></para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSpec_DataSchema"> <summary> <para> <para>A JSON string that represents the schema for an Amazon Redshift <code>DataSource</code>. The <code>DataSchema</code> defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.</para><para>A <code>DataSchema</code> is not required if you specify a <code>DataSchemaUri</code>.</para><para>Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to define your <code>DataSchema</code>.</para><para>{ "version": "1.0",</para><para> "recordAnnotationFieldName": "F1",</para><para> "recordWeightFieldName": "F2",</para><para> "targetFieldName": "F3",</para><para> "dataFormat": "CSV",</para><para> "dataFileContainsHeader": true,</para><para> "attributes": [</para><para> { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],</para><para> "excludedVariableNames": [ "F6" ] } </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSpec_DataSchemaUri"> <summary> <para> <para>Describes the schema location for an Amazon Redshift <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSourceId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSourceName"> <summary> <para> <para>A user-supplied name or description of the <code>DataSource</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DatabaseCredentials_Password"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.RoleARN"> <summary> <para> <para>A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following: </para><para><ul><li><para>A security group to allow Amazon ML to execute the <code>SelectSqlQuery</code> query on an Amazon Redshift cluster</para></li><li><para>An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the <code>S3StagingLocation</code></para></li></ul></para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSpec_S3StagingLocation"> <summary> <para> <para>Describes an Amazon S3 location to store the result set of the <code>SelectSqlQuery</code> query.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DataSpec_SelectSqlQuery"> <summary> <para> <para>Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.DatabaseCredentials_Username"> <summary> <para> The service has not provided documentation for this parameter; please refer to the service's API reference documentation for the latest available information. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromRedshiftCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet"> <summary> Creates a <code>DataSource</code> object. A <code>DataSource</code> references data that can be used to perform <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations. <para><code>CreateDataSourceFromS3</code> is an asynchronous operation. In response to <code>CreateDataSourceFromS3</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the <code>DataSource</code> has been created and is ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can be used to perform only <code>CreateMLModel</code>, <code>CreateEvaluation</code> or <code>CreateBatchPrediction</code> operations. </para><para> If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response. </para><para> The observation data used in a <code>DataSource</code> should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the <code>DataSource</code>. </para><para> After the <code>DataSource</code> has been created, it's ready to use in evaluations and batch predictions. If you plan to use the <code>DataSource</code> to train an <code>MLModel</code>, the <code>DataSource</code> also needs a recipe. A recipe describes how each input variable will be used in training an <code>MLModel</code>. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.ComputeStatistic"> <summary> <para> <para>The compute statistics for a <code>DataSource</code>. The statistics are generated from the observation data referenced by a <code>DataSource</code>. Amazon ML uses the statistics internally during <code>MLModel</code> training. This parameter must be set to <code>true</code> if the <code></code>DataSource<code></code> needs to be used for <code>MLModel</code> training.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.DataSpec_DataLocationS3"> <summary> <para> <para>The location of the data file(s) used by a <code>DataSource</code>. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.DataSpec_DataRearrangement"> <summary> <para> <para>A JSON string that represents the splitting and rearrangement processing to be applied to a <code>DataSource</code>. If the <code>DataRearrangement</code> parameter is not provided, all of the input data is used to create the <code>Datasource</code>.</para><para>There are multiple parameters that control what data is used to create a datasource:</para><ul><li><para><b><code>percentBegin</code></b></para><para>Use <code>percentBegin</code> to indicate the beginning of the range of the data used to create the Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when creating the datasource.</para></li><li><para><b><code>percentEnd</code></b></para><para>Use <code>percentEnd</code> to indicate the end of the range of the data used to create the Datasource. If you do not include <code>percentBegin</code> and <code>percentEnd</code>, Amazon ML includes all of the data when creating the datasource.</para></li><li><para><b><code>complement</code></b></para><para>The <code>complement</code> parameter instructs Amazon ML to use the data that is not included in the range of <code>percentBegin</code> to <code>percentEnd</code> to create a datasource. The <code>complement</code> parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for <code>percentBegin</code> and <code>percentEnd</code>, along with the <code>complement</code> parameter.</para><para>For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":0, "percentEnd":25}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}</code></para></li><li><para><b><code>strategy</code></b></para><para>To change how Amazon ML splits the data for a datasource, use the <code>strategy</code> parameter.</para><para>The default value for the <code>strategy</code> parameter is <code>sequential</code>, meaning that Amazon ML takes all of the data records between the <code>percentBegin</code> and <code>percentEnd</code> parameters for the datasource, in the order that the records appear in the input data.</para><para>The following two <code>DataRearrangement</code> lines are examples of sequentially ordered training and evaluation datasources:</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}</code></para><para>To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the <code>strategy</code> parameter to <code>random</code> and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between <code>percentBegin</code> and <code>percentEnd</code>. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.</para><para>The following two <code>DataRearrangement</code> lines are examples of non-sequentially ordered training and evaluation datasources:</para><para>Datasource for evaluation: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}</code></para><para>Datasource for training: <code>{"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}</code></para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.DataSpec_DataSchema"> <summary> <para> <para> A JSON string that represents the schema for an Amazon S3 <code>DataSource</code>. The <code>DataSchema</code> defines the structure of the observation data in the data file(s) referenced in the <code>DataSource</code>.</para><para>You must provide either the <code>DataSchema</code> or the <code>DataSchemaLocationS3</code>.</para><para>Define your <code>DataSchema</code> as a series of key-value pairs. <code>attributes</code> and <code>excludedVariableNames</code> have an array of key-value pairs for their value. Use the following format to define your <code>DataSchema</code>.</para><para>{ "version": "1.0",</para><para> "recordAnnotationFieldName": "F1",</para><para> "recordWeightFieldName": "F2",</para><para> "targetFieldName": "F3",</para><para> "dataFormat": "CSV",</para><para> "dataFileContainsHeader": true,</para><para> "attributes": [</para><para> { "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],</para><para> "excludedVariableNames": [ "F6" ] } </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.DataSpec_DataSchemaLocationS3"> <summary> <para> <para>Describes the schema location in Amazon S3. You must provide either the <code>DataSchema</code> or the <code>DataSchemaLocationS3</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.DataSourceId"> <summary> <para> <para>A user-supplied identifier that uniquely identifies the <code>DataSource</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.DataSourceName"> <summary> <para> <para>A user-supplied name or description of the <code>DataSource</code>. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLDataSourceFromS3Cmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLEvaluationCmdlet"> <summary> Creates a new <code>Evaluation</code> of an <code>MLModel</code>. An <code>MLModel</code> is evaluated on a set of observations associated to a <code>DataSource</code>. Like a <code>DataSource</code> for an <code>MLModel</code>, the <code>DataSource</code> for an <code>Evaluation</code> contains values for the <code>Target Variable</code>. The <code>Evaluation</code> compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the <code>MLModel</code> functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding <code>MLModelType</code>: <code>BINARY</code>, <code>REGRESSION</code> or <code>MULTICLASS</code>. <para><code>CreateEvaluation</code> is an asynchronous operation. In response to <code>CreateEvaluation</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to <code>PENDING</code>. After the <code>Evaluation</code> is created and ready for use, Amazon ML sets the status to <code>COMPLETED</code>. </para><para> You can use the <code>GetEvaluation</code> operation to check progress of the evaluation during the creation operation. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLEvaluationCmdlet.EvaluationDataSourceId"> <summary> <para> <para>The ID of the <code>DataSource</code> for the evaluation. The schema of the <code>DataSource</code> must match the schema used to create the <code>MLModel</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLEvaluationCmdlet.EvaluationId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>Evaluation</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLEvaluationCmdlet.EvaluationName"> <summary> <para> <para>A user-supplied name or description of the <code>Evaluation</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLEvaluationCmdlet.MLModelId"> <summary> <para> <para>The ID of the <code>MLModel</code> to evaluate.</para><para>The schema used in creating the <code>MLModel</code> must match the schema of the <code>DataSource</code> used in the <code>Evaluation</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLEvaluationCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet"> <summary> Creates a new <code>MLModel</code> using the <code>DataSource</code> and the recipe as information sources. <para> An <code>MLModel</code> is nearly immutable. Users can update only the <code>MLModelName</code> and the <code>ScoreThreshold</code> in an <code>MLModel</code> without creating a new <code>MLModel</code>. </para><para><code>CreateMLModel</code> is an asynchronous operation. In response to <code>CreateMLModel</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>MLModel</code> status to <code>PENDING</code>. After the <code>MLModel</code> has been created and ready is for use, Amazon ML sets the status to <code>COMPLETED</code>. </para><para> You can use the <code>GetMLModel</code> operation to check the progress of the <code>MLModel</code> during the creation operation. </para><para><code>CreateMLModel</code> requires a <code>DataSource</code> with computed statistics, which can be created by setting <code>ComputeStatistics</code> to <code>true</code> in <code>CreateDataSourceFromRDS</code>, <code>CreateDataSourceFromS3</code>, or <code>CreateDataSourceFromRedshift</code> operations. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.MLModelId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.MLModelName"> <summary> <para> <para>A user-supplied name or description of the <code>MLModel</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.MLModelType"> <summary> <para> <para>The category of supervised learning that this <code>MLModel</code> will address. Choose from the following types:</para><ul><li>Choose <code>REGRESSION</code> if the <code>MLModel</code> will be used to predict a numeric value.</li><li>Choose <code>BINARY</code> if the <code>MLModel</code> result has two possible values.</li><li>Choose <code>MULTICLASS</code> if the <code>MLModel</code> result has a limited number of values. </li></ul><para> For more information, see the <a href="http://docs.aws.amazon.com/machine-learning/latest/dg">Amazon Machine Learning Developer Guide</a>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.Parameter"> <summary> <para> <para>A list of the training parameters in the <code>MLModel</code>. The list is implemented as a map of key-value pairs.</para><para>The following is the current set of training parameters: </para><ul><li><para><code>sgd.maxMLModelSizeInBytes</code> - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.</para><para> The value is an integer that ranges from <code>100000</code> to <code>2147483648</code>. The default value is <code>33554432</code>.</para></li><li><para><code>sgd.maxPasses</code> - The number of times that the training process traverses the observations to build the <code>MLModel</code>. The value is an integer that ranges from <code>1</code> to <code>10000</code>. The default value is <code>10</code>.</para></li><li><para><code>sgd.shuffleType</code> - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are <code>auto</code> and <code>none</code>. The default value is <code>none</code>. We strongly recommend that you shuffle your data.</para></li><li><para><code>sgd.l1RegularizationAmount</code> - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</para><para>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L1 normalization. This parameter can't be used when <code>L2</code> is specified. Use this parameter sparingly.</para></li><li><para><code>sgd.l2RegularizationAmount</code> - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as <code>1.0E-08</code>.</para><para>The value is a double that ranges from <code>0</code> to <code>MAX_DOUBLE</code>. The default is to not use L2 normalization. This parameter can't be used when <code>L1</code> is specified. Use this parameter sparingly.</para></li></ul> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.Recipe"> <summary> <para> <para>The data recipe for creating the <code>MLModel</code>. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.RecipeUri"> <summary> <para> <para>The Amazon Simple Storage Service (Amazon S3) location and file name that contains the <code>MLModel</code> recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.TrainingDataSourceId"> <summary> <para> <para>The <code>DataSource</code> that points to the training data.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLModelCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.NewMLRealtimeEndpointCmdlet"> <summary> Creates a real-time endpoint for the <code>MLModel</code>. The endpoint contains the URI of the <code>MLModel</code>; that is, the location to send real-time prediction requests for the specified <code>MLModel</code>. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLRealtimeEndpointCmdlet.MLModelId"> <summary> <para> <para>The ID assigned to the <code>MLModel</code> during creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.NewMLRealtimeEndpointCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.RemoveMLBatchPredictionCmdlet"> <summary> Assigns the DELETED status to a <code>BatchPrediction</code>, rendering it unusable. <para> After using the <code>DeleteBatchPrediction</code> operation, you can use the <a>GetBatchPrediction</a> operation to verify that the status of the <code>BatchPrediction</code> changed to DELETED. </para><para><b>Caution:</b> The result of the <code>DeleteBatchPrediction</code> operation is irreversible. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLBatchPredictionCmdlet.BatchPredictionId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>BatchPrediction</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLBatchPredictionCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.RemoveMLDataSourceCmdlet"> <summary> Assigns the DELETED status to a <code>DataSource</code>, rendering it unusable. <para> After using the <code>DeleteDataSource</code> operation, you can use the <a>GetDataSource</a> operation to verify that the status of the <code>DataSource</code> changed to DELETED. </para><para><b>Caution:</b> The results of the <code>DeleteDataSource</code> operation are irreversible. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLDataSourceCmdlet.DataSourceId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>DataSource</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLDataSourceCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.RemoveMLEvaluationCmdlet"> <summary> Assigns the <code>DELETED</code> status to an <code>Evaluation</code>, rendering it unusable. <para> After invoking the <code>DeleteEvaluation</code> operation, you can use the <code>GetEvaluation</code> operation to verify that the status of the <code>Evaluation</code> changed to <code>DELETED</code>. </para><caution><title>Caution</title><para> The results of the <code>DeleteEvaluation</code> operation are irreversible. </para></caution> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLEvaluationCmdlet.EvaluationId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>Evaluation</code> to delete.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLEvaluationCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.RemoveMLModelCmdlet"> <summary> Assigns the <code>DELETED</code> status to an <code>MLModel</code>, rendering it unusable. <para> After using the <code>DeleteMLModel</code> operation, you can use the <code>GetMLModel</code> operation to verify that the status of the <code>MLModel</code> changed to DELETED. </para><para><b>Caution:</b> The result of the <code>DeleteMLModel</code> operation is irreversible. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLModelCmdlet.MLModelId"> <summary> <para> <para>A user-supplied ID that uniquely identifies the <code>MLModel</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLModelCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.RemoveMLRealtimeEndpointCmdlet"> <summary> Deletes a real time endpoint of an <code>MLModel</code>. </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLRealtimeEndpointCmdlet.MLModelId"> <summary> <para> <para>The ID assigned to the <code>MLModel</code> during creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLRealtimeEndpointCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.RemoveMLResourceTagCmdlet"> <summary> Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags. <para> If you specify a tag that doesn't exist, Amazon ML ignores it. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLResourceTagCmdlet.ResourceId"> <summary> <para> <para>The ID of the tagged ML object. For example, <code>exampleModelId</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLResourceTagCmdlet.ResourceType"> <summary> <para> <para>The type of the tagged ML object.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLResourceTagCmdlet.TagKey"> <summary> <para> <para>One or more tags to delete.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.RemoveMLResourceTagCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.UpdateMLBatchPredictionCmdlet"> <summary> Updates the <code>BatchPredictionName</code> of a <code>BatchPrediction</code>. <para> You can use the <code>GetBatchPrediction</code> operation to view the contents of the updated data element. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLBatchPredictionCmdlet.BatchPredictionId"> <summary> <para> <para>The ID assigned to the <code>BatchPrediction</code> during creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLBatchPredictionCmdlet.BatchPredictionName"> <summary> <para> <para>A new user-supplied name or description of the <code>BatchPrediction</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLBatchPredictionCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.UpdateMLDataSourceCmdlet"> <summary> Updates the <code>DataSourceName</code> of a <code>DataSource</code>. <para> You can use the <code>GetDataSource</code> operation to view the contents of the updated data element. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLDataSourceCmdlet.DataSourceId"> <summary> <para> <para>The ID assigned to the <code>DataSource</code> during creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLDataSourceCmdlet.DataSourceName"> <summary> <para> <para>A new user-supplied name or description of the <code>DataSource</code> that will replace the current description. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLDataSourceCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.UpdateMLEvaluationCmdlet"> <summary> Updates the <code>EvaluationName</code> of an <code>Evaluation</code>. <para> You can use the <code>GetEvaluation</code> operation to view the contents of the updated data element. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLEvaluationCmdlet.EvaluationId"> <summary> <para> <para>The ID assigned to the <code>Evaluation</code> during creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLEvaluationCmdlet.EvaluationName"> <summary> <para> <para>A new user-supplied name or description of the <code>Evaluation</code> that will replace the current content. </para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLEvaluationCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> <member name="T:Amazon.PowerShell.Cmdlets.ML.UpdateMLMLModelCmdlet"> <summary> Updates the <code>MLModelName</code> and the <code>ScoreThreshold</code> of an <code>MLModel</code>. <para> You can use the <code>GetMLModel</code> operation to view the contents of the updated data element. </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLMLModelCmdlet.MLModelId"> <summary> <para> <para>The ID assigned to the <code>MLModel</code> during creation.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLMLModelCmdlet.MLModelName"> <summary> <para> <para>A user-supplied name or description of the <code>MLModel</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLMLModelCmdlet.ScoreThreshold"> <summary> <para> <para>The <code>ScoreThreshold</code> used in binary classification <code>MLModel</code> that marks the boundary between a positive prediction and a negative prediction.</para><para>Output values greater than or equal to the <code>ScoreThreshold</code> receive a positive result from the <code>MLModel</code>, such as <code>true</code>. Output values less than the <code>ScoreThreshold</code> receive a negative response from the <code>MLModel</code>, such as <code>false</code>.</para> </para> </summary> </member> <member name="P:Amazon.PowerShell.Cmdlets.ML.UpdateMLMLModelCmdlet.Force"> <summary> This parameter overrides confirmation prompts to force the cmdlet to continue its operation. This parameter should always be used with caution. </summary> </member> </members> </doc> |