Random Forest Feature Importance Evaluation - Platform For AI

The Random Forest Feature Importance Evaluation component allows you to use raw data and a random forest model to calculate feature importance.

Configure the component

You can use one of the following methods to configure the Random Forest Feature Importance Evaluation component.

Method 1: Configure the component on the pipeline page

You can configure the parameters of the Random Forest Feature Importance Evaluation component on the pipeline page of Machine Learning Designer of Machine Learning Platform for AI (PAI). Machine Learning Designer is formerly known as Machine Learning Studio. The following table describes the parameters.

Tab	Parameter	Description
Fields Setting	Feature Columns	Optional. The feature columns that are selected from the input table for training. By default, all columns other than the label column are selected.
Fields Setting	Target Column	Required. The label column. Click the icon. In the Select Column dialog box, enter the keywords of the column that you want to search for. Select the column and click OK.
Parameters Setting	Parallel Computing Cores	Optional. The number of cores used in parallel computing.
Parameters Setting	Memory Size per Core	Optional. The memory size of each core. Unit: MB.

Method 2: Use PAI commands

Configure the component parameters by using PAI commands. You can use the SQL Script component to call PAI commands. For more information, see SQL Script.

pai -name feature_importance -project algo_public
    -DinputTableName=pai_dense_10_10
    -DmodelName=xlab_m_random_forests_1_20318_v0
    -DoutputTableName=erkang_test_dev.pai_temp_2252_20319_1
    -DlabelColName=y
    -DfeatureColNames="pdays,previous,emp_var_rate,cons_price_idx,cons_conf_idx,euribor3m,nr_employed,age,campaign,poutcome"
    -Dlifecycle=28 ;

Parameter	Required	Description	Default value
inputTableName	Yes	The name of the input table.	No default value
outputTableName	Yes	The name of the output table.	No default value
labelColName	Yes	The name of the label column in the input table.	No default value
modelName	Yes	The name of the input model.	No default value
featureColNames	No	The feature columns that are selected from the input table for training.	All columns other than the label column
inputTablePartitions	No	The partitions that are selected from the input table for training.	All partitions
lifecycle	No	The lifecycle of the output table.	Not specified
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

Execute the following SQL statements to generate training data:

drop table if exists pai_dense_10_10;
create table if not exists pai_dense_10_10 as
select
    age,campaign,pdays, previous, poutcome, emp_var_rate, cons_price_idx, cons_conf_idx, euribor3m, nr_employed, y
from  bank_data limit 10;

Create the experiment shown in the following figure. For more information, see Algorithm modeling.
The data source is pai_dense_10_10. y is the label column of the random forest model, and other columns are feature columns. Select age and campaign for the Columns Forced to Convert parameter. This indicates that the two columns are processed as enumerated features, and default settings are retained for other columns.
Run the experiment and view the prediction results.
After the experiment is run, right-click the Random Forest Feature Importance Evaluation component and select View Analytics Report to view the result.