随机森林回归

概述

“随机决策森林回归”节点用于产生回归模型。随机决策森林是用随机的方式建立一个森林模型，森林由很多的决策树组成，每棵决策树之间没有关联。当有一个新的样本输入时，该样本取值为所有决策树的预测值的平均值。

随机决策森林回归中的决策树算法是递归地构建决策树的过程，用平方误差最小准则，进行特征选择，生成二叉树。平方误差计算公式如下：

其中 是样本类标的均值，y_i 是样本的标签，N 是样本数量。

输入

输出

spark pipeline类型的模型

参数说明

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": ""}
    "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": ""}
    "impurity": "variance",
    "subsampling_rate": 1.0,  # @param {"label": "subsampling_rate", "type": "number", "required": "true", "range": "(0.0,1.0]", "helpTip": ""}
    "num_trees": 20,  # @param {"label": "num_trees", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}
    "feature_subset_strategy": "all"  # @param {"label": "feature_subset_strategy", "type": "enum", "required": "true", "options":"auto,all,onethird,sqrt,log2", "helpTip": ""}
}
rf_regressor____id___ = MLSRandomForestRegression(**params)
rf_regressor____id___.run()
# @output {"label":"pipeline_model","name":"rf_regressor____id___.get_outputs()['output_port_1']","type":"PipelineModel"}