更新时间:2022-09-01 GMT+08:00
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决策树分类

概述

“决策树分类”节点用于产生二分类或多分类模型。

决策树是附加概率结果的一个树状的决策图,是直观的运用统计概率分析的图法,树中的每一个节点表示对象属性的判断条件,其分支表示符合节点条件的对象,树的叶子节点表示对象所属的预测结果。其通过基尼不纯度(Gini impurity)或熵(Entropy)来对一个集合的有序程度进行量化,并对一次拆分进行量化评价。

输入

参数

子参数

参数说明

inputs

dataframe

inputs为字典类型,dataframe为pyspark中的DataFrame类型对象

输出

spark pipeline类型的模型

参数说明

参数

子参数

参数说明

b_use_default_encoder

-

是否使用默认编码,默认为True

input_features_str

-

输入的特征列名以逗号分隔组成的格式化字符串,例如:

"column_a"

"column_a,column_b"

label_col

-

目标列

classifier_label_index_col

-

目标列经过标签编码后的新的列名,默认为"label_index"

classifier_feature_vector_col

-

算子输入的特征向量列的列名,默认为"model_features"

prediction_index_col

-

算子输出的预测label对应的标签列,默认为"prediction_index"

prediction_col

-

算子输出的预测label的列名,默认为"prediction"

max_depth

-

树的最大深度,默认为5

max_bins

-

最大分箱数,默认为32

min_instances_per_node

-

树节点分割时要求子节点包含的最小实例数,默认为1

min_info_gain

-

最小信息增益,默认为0

impurity

-

不纯度,支持entropy、gini,默认为"gini"

样例

inputs = {
    "dataframe": None  # @input {"label":"dataframe","type":"DataFrame"}
}
params = {
    "inputs": inputs,
    "b_output_action": True,
    "b_use_default_encoder": True,  # @param {"label": "b_use_default_encoder", "type": "boolean", "required": "true", "helpTip": ""}
    "input_features_str": "",  # @param {"label": "input_features_str", "type": "string", "required": "false", "helpTip": ""}
    "outer_pipeline_stages": None,
    "label_col": "",  # @param {"label": "label_col", "type": "string", "required": "true", "helpTip": ""}
    "classifier_label_index_col": "label_index",  # @param {"label": "classifier_label_index_col", "type": "string", "required": "true", "helpTip": ""}
    "classifier_feature_vector_col": "model_features",  # @param {"label": "classifier_feature_vector_col", "type": "string", "required": "true", "helpTip": ""}
    "prediction_index_col": "prediction_index",  # @param {"label": "prediction_index_col", "type": "string", "required": "true", "helpTip": ""}
    "prediction_col": "prediction",  # @param {"label": "prediction_col", "type": "string", "required": "true", "helpTip": ""}
    "max_depth": 5,  # @param {"label": "max_depth", "type": "integer", "required": "true", "range":"(0,2147483647]", "helpTip": ""}
    "max_bins": 32,  # @param {"label": "max_bins", "type": "integer", "required": "true", "range":"(0,2147483647]", "helpTip": ""}
    "min_instances_per_node": 1,  # @param {"label": "min_instances_per_node", "type": "integer", "required": "true", "range": "[1,2147483647]", "helpTip": ""}
    "min_info_gain": 0.0,  # @param {"label": "min_info_gain", "type": "number", "required": "true", "range": "[0,none)", "helpTip": ""}
    "impurity": "gini"  # @param {"label": "impurity", "type": "enum", "required": "true", "options": "entropy,gini", "helpTip": ""}
}
dt_classifier____id___ = MLSDecisionTreeClassifier(**params)
dt_classifier____id___.run()
# @output {"label":"pipeline_model","name":"dt_classifier____id___.get_outputs()['output_port_1']","type":"PipelineModel"}

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