The Binary Classification Evaluation component is used to calculate AUC, KS, and F1 score metrics to generate Kolmogorov–Smirnov (KS) curves, precision-recall (P-R) curves, ROC curves, lift charts, and gain charts.

Configure the component

You can configure the component by using one of the following methods:
  • Use the Machine Learning Platform for AI console
    Parameter Description
    Original Label Column The name of the objective column.
    Score Column The prediction score column. Default value: prediction_score.
    Positive Sample Label Specifies whether the samples are positive samples.
    Number of Bins with Same Frequency when Calculating Indexes such as KS and PR The number of bins obtained by using the equal frequency binning method.
    Grouping Column The group ID column. This parameter is used to calculate evaluation metrics for each group.
    Advanced Options If you select Advanced Options, the Prediction Result Detail Column, Prediction Targets Consistent With Evaluation Targets, and Save Performance Index parameters are valid.
    Prediction Result Detail Column The name of the prediction result detail column.
    Prediction Targets Consistent with Evaluation Targets Specifies whether the prediction objective is consistent with the evaluation objective. For example, in a financial scenario, a program is trained to predict the probability that a customer is bad. The larger the probability is, the more likely the customer is bad. Related metrics such as lift evaluate the bad-customer detection rate. In this case, the prediction objective is consistent with the evaluation objective. In a credit scoring scenario, a program is trained to predict the probability that a customer is good. The larger the probability is, the more likely the customer is good. However, related metrics evaluate the bad-customer detection rate. In this case, the prediction objective is inconsistent with the evaluation objective.
    Save Performance Index Specifies whether to save performance metrics.
  • Use commands
    PAI -name=evaluate -project=algo_public
        -DoutputMetricTableName=output_metric_table
        -DoutputDetailTableName=output_detail_table
        -DinputTableName=input_data_table
        -DlabelColName=label
        -DscoreColName=score
    Parameter Required Description Default value
    inputTableName Yes The name of the input table. N/A
    inputTablePartitions No The partitions that are selected from the input table for training. Full table
    labelColName Yes The name of the objective column. N/A
    scoreColName Yes The name of the score column. N/A
    groupColName No The name of the group column. This parameter is used for the evaluation of each group. N/A
    binCount No The number of bins obtained by using the equal frequency binning method during the calculation of metrics such as KS and PR. 1000
    outputMetricTableName Yes The output metric table. The metrics include AUC, KS, and F1 score. N/A
    outputDetailTableName No The detail data table that is generated. N/A
    positiveLabel No Specifies whether the samples are positive samples. 1
    lifecycle No The lifecycle of the output table. N/A
    coreNum No The number of cores. Determined by the system
    memSizePerCore No The memory size of each core. Determined by the system