The common logistic regression algorithm is used for binary classification. Machine Learning Platform for AI (PAI) allows you to use the logistic regression algorithm for multiclass classification. The Logistic Regression for Multiclass Classification component supports both the sparse and dense data formats.

Configure the component

You can configure the component by using one of the following methods:
  • Use the PAI console
    Tab Parameter Description
    Fields Setting Training Feature Columns The feature columns that are selected from the data source for training. The columns of the DOUBLE and BIGINT types are supported.
    Note A maximum of 20 million features are supported.
    Target Columns The objective columns in the input table.
    Sparse Format Specifies whether the input data is in the sparse format.
    Parameters Setting Regularization Type Valid values: L1, L2, and None.
    Maximum Number of Iterations The maximum number of iterations. Default value: 100.
    Regularization Coefficient If the Regularization Type parameter is set to None, this parameter is invalid.
    Minimum Convergence Deviance The minimum convergence deviance. Default value: 0.000001.
  • Use commands
    PAI -name logisticregression_multi
        -project algo_public
        -DmodelName="xlab_m_logistic_regression_6096"
        -DregularizedLevel="1"
        -DmaxIter="100"
        -DregularizedType="l1"
        -Depsilon="0.000001"
        -DlabelColName="y"
        -DfeatureColNames="pdays,emp_var_rate"
        -DgoodValue="1"
        -DinputTableName="bank_data"
    Parameter Required Description Default value
    inputTableName Yes The name of the input table. N/A
    featureColNames No The feature columns that are selected from the input table for training.
    Note A maximum of 20 million features are supported.
    All columns of numeric data types
    labelColName Yes The name of the label column that is selected from the input table. N/A
    inputTablePartitions No The partitions that are selected from the input table for training. Specify this parameter in one of the following formats:
    • partition_name=value
    • name1=value1/name2=value2: multi-level partitions
    Note If you specify multiple partitions, separate them with commas (,).
    Full table
    modelName Yes The name of the output model. N/A
    regularizedType No The regularization type. Valid values: l1, l2, and None. l1
    regularizedLevel No The regularization coefficient. If the regularizedType parameter is set to None, this parameter is invalid. 1.0
    maxIter No The maximum number of iterations of the limited-memory BFGS (L-BFGS) algorithm. 100
    epsilon No The convergence error. This parameter specifies the conditions to terminate the L-BFGS algorithm. If the difference of log-likelihood between two iterations is less than the value specified by this parameter, the iteration of the L-BFGS algorithm is terminated. 1.0e-06
    enableSparse No Specifies whether the input data is in the sparse format. Valid values: true and false. false
    itemDelimiter No The delimiter that is used to separate key-value pairs when data in the input data is in the sparse format. ,
    kvDelimiter No The delimiter that is used to separate keys and values when data in the input table is in the sparse format. :
    coreNum No The number of cores. Determined by the system
    memSizePerCore No The memory size of each core. Unit: MB. Determined by the system

Example

  1. Execute the following SQL statements to generate training data:
    drop table if exists multi_lr_test_input;
    create table multi_lr_test_input
    as
    select
        *
    from
    (
        select
            cast(1 as double) as f0,
            cast(0 as double) as f1,
            cast(0 as double) as f2,
            cast(0 as double) as f3,
            cast(0 as bigint) as label
        from dual
        union all
            select
                cast(0 as double) as f0,
                cast(1 as double) as f1,
                cast(0 as double) as f2,
                cast(0 as double) as f3,
                cast(0 as bigint) as label
        from dual
        union all
            select
                cast(0 as double) as f0,
                cast(0 as double) as f1,
                cast(1 as double) as f2,
                cast(0 as double) as f3,
                cast(2 as bigint) as label
        from dual
        union all
            select
                cast(0 as double) as f0,
                cast(0 as double) as f1,
                cast(0 as double) as f2,
                cast(1 as double) as f3,
                cast(1 as bigint) as label
        from dual
    ) a;
    The following table provides the generated training data in the multi_lr_test_input table.
    f0 f1 f2 f3 label
    1.0 0.0 0.0 0.0 0
    0.0 0.0 1.0 0.0 2
    0.0 0.0 0.0 1.0 1
    0.0 1.0 0.0 0.0 0
  2. Run the following PAI command to submit the parameters of the Logistic Regression for Multiclass Classification component:
    drop offlinemodel if exists multi_lr_test_model;
    PAI -name logisticregression_multi
        -project algo_public
        -DmodelName="multi_lr_test_model"
        -DitemDelimiter=","
        -DregularizedLevel="1"
        -DmaxIter="100"
        -DregularizedType="None"
        -Depsilon="0.000001"
        -DkvDelimiter=":"
        -DlabelColName="label"
        -DfeatureColNames="f0,f1,f2,f3"
        -DenableSparse="false"
        -DinputTableName="multi_lr_test_input";
  3. Run the following PAI command to submit the parameters of the Prediction component:
    drop table if exists multi_lr_test_prediction_result;
    PAI -name prediction
        -project algo_public
        -DdetailColName="prediction_detail"
        -DmodelName="multi_lr_test_model"
        -DitemDelimiter=","
        -DresultColName="prediction_result"
        -Dlifecycle="28"
        -DoutputTableName="multi_lr_test_prediction_result"
        -DscoreColName="prediction_score"
        -DkvDelimiter=":"
        -DinputTableName="multi_lr_test_input"
        -DenableSparse="false"
        -DappendColNames="label";
  4. View the multi_lr_test_prediction_result table.
    label prediction_result prediction_score prediction_detail
    0 0 0.9999997274902165 {"0": 0.9999997274902165, "1": 2.324679066261573e-07, "2": 2.324679066261569e-07}
    0 0 0.9999997274902165 {"0": 0.9999997274902165, "1": 2.324679066261573e-07, "2": 2.324679066261569e-07}
    2 2 0.9999999155958832 {"0": 2.018833979850994e-07, "1": 2.324679066261573e-07, "2": 0.9999999155958832}
    1 1 0.9999999155958832 {"0": 2.018833979850994e-07, "1": 0.9999999155958832, "2": 2.324679066261569e-07}