The Regression Model Evaluation component is used to evaluate the advantages and disadvantages of the different models of regression algorithms based on prediction results and original results. Then, evaluation metrics and histograms of residuals are generated.

Regression Model Evaluation

You can configure the component by using one of the following methods:
  • Use the Machine Learning Platform for AI console
    Tab Parameter Description
    Fields Setting Original Regression Value The columns of numeric data types are supported.
    Predicted Regression Value The columns of numeric data types are supported.
    Tuning Worker number The number of cores. Valid values: 1 to 9999. This parameter must be used with the Memory Size per Node parameter.
    Memory Size per Node The memory size of each core. Valid values: 1024 to 64 × 1024. Unit: MB.
  • Use commands
    PAI -name regression_evaluation -project algo_public
        -DinputTableName=input_table
        -DyColName=y_col
        -DpredictionColName=prediction_col
        -DindexOutputTableName=index_output_table
        -DresidualOutputTableName=residual_output_table;
    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 computing. Full table
    yColName Yes The name of the column that contains original dependent variables in the input table. The columns of numeric data types are supported. N/A
    predictionColName Yes The name of the column that contains dependent variables in the prediction result. The columns of numeric data types are supported. N/A
    indexOutputTableName Yes The name of the output table of regression metrics. N/A
    residualOutputTableName Yes The name of the output table of the histogram of residuals. N/A
    intervalNum No The number of intervals of the histogram. 100
    lifecycle No The lifecycle of the output table. The value of this parameter must be a positive integer. N/A
    coreNum No The number of cores. Valid values: 1 to 9999. Determined by the system
    memSizePerCore No The memory size of each core. Valid values: 1024 to 64 × 1024. Unit: MB. Determined by the system

Output

The output table of regression metrics is generated in the JSON format and contains the following parameters.
Parameter Description
SST The total sum of squares.
SSE The sum of squared errors.
SSR The sum of squares due to regression.
R2 The coefficient of determination.
R The coefficient of multiple correlations.
MSE The mean-square error.
RMSE The root-mean-square error.
MAE The mean absolute error.
MAD The mean error.
MAPE The mean absolute percentage error.
count The number of rows.
yMean The mean of original dependent variables.
predictionMean The mean of prediction results.