华为云AI开发平台ModelArts随机森林回归_云淘科技
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,yi 是样本的标签,N 是样本数量。
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 |
子参数说明 |
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 |
num_trees |
– |
树的个数,默认为20 |
feature_subset_strategy |
– |
节点分割时考虑用到的特征列的策略,支持auto、all、onethird、sqrt、log2、n,默认为”all” |
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"}
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