Configure Feature Importance Filtering to filter top N features - Platform For AI:Feature Importance Filtering

The Feature Importance Filtering component provides the filtering feature for components including Linear Model Feature Importance, GBDT Feature Importance, and Random Forest Feature Importance. The Feature Importance Filtering component can be used to filter the top N features.

Configure the component

Machine Learning Platform for AI (PAI) command

PAI -name fe_filter_runner -project algo_public
    -DselectedCols=pdays,previous,emp_var_rate,cons_price_idx,cons_conf_idx,euribor3m,nr_employed,age,campaign,poutcome
    -DinputTable=pai_dense_10_10
    -DweightTable=pai_temp_2252_20319_1
    -DtopN=5
    -DmodelTable=pai_temp_2252_20320_2
    -DoutputTable=pai_temp_2252_20320_1;

Parameter	Description	Required
inputTable	The name of the input table.	Yes
inputTablePartitions	The partitions in the input table. By default, all partitions are selected. Specify a single partition in the format of `partition_name=value`. Specify multiple partitions in the format of `name1=value1,name2=value2`. Note Separate multiple partitions with commas (,). Specify multi-level partitions in the format of `name1=value1/name2=value2`.	No
weightTable	The weight tables of the feature importance. The weight tables are the output tables of the Linear Model Feature Importance, GBDT Feature Importance, and Random Forest Feature Importance components.	Yes
outputTable	The output table after the top N features are filtered.	Yes
modelTable	The model file generated by feature filtering.	Yes
selectedCols	By default, all the fields in the input table are selected.	No
topN	The top N features that are filtered. Default value: 10. Note The value of this parameter must be a positive integer.	No
lifecycle	The lifecycle of the output table. Default value: 7. Note The value of this parameter must be a positive integer.	No