更新时间：2023-05-05 GMT+08:00
决策树分类特征重要性
概述
采用决策树分类算法计算数据集特征的特征重要性。
输入
参数 |
子参数 |
参数说明 |
|---|---|---|
inputs |
dataframe |
参数必选，表示输入的数据集。 如果没有pipeline_model和decision_tree_classify_model参数，表示直接根据数据集训练决策树分类算法得到特征重要性 |
pipeline_model |
参数可选，如果含有该参数，表示根据上游的pyspark pipeline模型对象来计算特征重要性 |
|
decision_tree_classify_model |
参数可选，如果含有该参数，表示根据上游的决策树分类模型对象来计算特征重要性 |
输出
包含特征重要性的结果数据集
参数说明
参数 |
子参数 |
参数说明 |
|---|---|---|
input_columns_str |
- |
数据集的特征列名组成的格式化字符串，例如: "column_a" "column_a,column_b" |
label_col |
- |
目标列名 |
model_input_features_col |
- |
特征向量的列名 |
classifier_label_index_col |
- |
将目标列按照标签编码后的列名，默认为"label_index" |
prediction_index_col |
- |
训练模型时，预测结果对应标签的列名，默认为"prediction_index" |
prediction_col |
- |
训练模型时，预测结果对应的列名，默认为"prediction" |
max_depth |
- |
树的最大深度 |
max_bins |
- |
分割特征时的最大分箱个数 |
min_instances_per_node |
- |
决策树分裂时要求每个节点必须包含的实例数目 |
min_info_gain |
- |
最小信息增益 |
impurity |
- |
计算信息增益的标准，支持"gini"和"entropy" |
样例
inputs = {
"dataframe": None, # @input {"label":"dataframe","type":"DataFrame"}
"pipeline_model": None, # @input {"label":"pipeline_model","type":"PipelineModel"}
"decision_tree_classify_model": None
}
params = {
"inputs": inputs,
"input_columns_str": "", # @param {"label":"input_columns_str","type":"string","required":"false","helpTip":""}
"label_col": "", # @param {"label":"label_col","type":"string","required":"true","helpTip":""}
"model_input_features_col": "model_features", # @param {"label":"model_input_features_col","type":"string","required":"false","helpTip":""}
"classifier_label_index_col": "label_index", # @param {"label":"classifier_label_index_col","type":"string","required":"false","helpTip":""}
"prediction_index_col": "prediction_index", # @param {"label":"prediction_index_col","type":"string","required":"false","helpTip":""}
"prediction_col": "prediction", # @param {"label":"prediction_col","type":"string","required":"false","helpTip":""}
"max_depth": 5, # @param {"label":"max_depth","type":"integer","required":"false","range":"(0,2147483647]","helpTip":""}
"max_bins": 32, # @param {"label":"max_bins","type":"integer","required":"false","range":"(0,2147483647]","helpTip":""}
"min_instances_per_node": 1, # @param {"label":"min_instances_per_node","type":"integer","required":"false","range":"(0,2147483647]","helpTip":""}
"min_info_gain": 0.0, # @param {"label":"min_info_gain","type":"number","required":"false","helpTip":""}
"impurity": "gini" # @param {"label":"impurity","type":"enum","required":"false","options":"entropy,gini","helpTip":""}
}
dt_classify_feature_importance____id___ = MLSDecisionTreeClassifierFeatureImportance(**params)
dt_classify_feature_importance____id___.run()
# @output {"label":"dataframe","name":"dt_classify_feature_importance____id___.get_outputs()['output_port_1']","type":"DataFrame"}
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