更新时间:2022-09-01 GMT+08:00
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梯度提升树回归

概述

“梯度提升树回归”节点用于生成回归模型,是一种基于决策树的迭代回归算法。该算法采用迭代的思想不断地构建决策树模型,每棵树都是通过梯度优化损失函数而构建,从而达到从基准值到目标值的逼近。算法思想可简单理解成:后一次模型都是针对前一次模型预测出错的情况进行修正,模型随着迭代不断地改进,从而获得比较好的预测效果。

梯度提升树回归的损失函数为均方差损失函数,如下所示:

其中,N 表示样本数量,xi 表示样本i 的特征,yi 表示样本i 的标签,F(xi) 表示样本i 预测的标签。

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

-

输入的列名以逗号分隔组成的字符串,例如:

"column_a"

"column_a,column_b"

label_col

-

目标列

regressor_feature_vector_col

-

算子输入的特征向量列的列名,默认为"model_features"

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

-

节点分割时,要求子节点必须包含的最少实例数,默认为1

min_info_gain

-

节点是否分割要求的最小信息增益,默认为0.0

subsampling_rate

-

学习每棵决策树用到的训练集的抽样比例,默认为1.0

loss_type

-

损失函数类型,支持squared、absolute,默认为"squared"

max_iter

-

最大迭代次数,默认为20

step_size

-

步长,默认为0.1

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": "target label column"}
    "regressor_feature_vector_col": "model_features",  # @param {"label": "regressor_feature_vector_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": "(0,2147483647]", "helpTip": ""}
    "min_info_gain": 0.0,  # @param {"label": "min_info_gain", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}
    "subsampling_rate": 1.0,  # @param {"label": "subsampling_rate", "type": "number", "required": "true", "range": "(0.0,1.0]", "helpTip": ""}
    "loss_type": "squared",  # @param {"label": "loss_type", "type": "enum", "required": "true", "options": "squared,absolute", "helpTip": ""}
    "max_iter": 20,  # @param {"label": "max_iter", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}
    "step_size": 0.1,  # @param {"label": "step_size", "type": "number", "required": "true", "range": "(0.0,none)", "helpTip": ""}
    "impurity": "variance"
}
gbt_regressor____id___ = MLSGBTRegression(**params)
gbt_regressor____id___.run()
# @output {"label":"pipeline_model","name":"gbt_regressor____id___.get_outputs()['output_port_1']","type":"PipelineModel"}
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