华为云AI开发平台ModelArts线性回归_云淘科技
概述
“线性回归”节点用于产生线性回归模型。它是利用数理统计中的回归分析，来确定两种或两种以上变数间相互依赖的定量关系的统计分析方法。
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_iter |
– |
最大迭代次数，默认为100 |
|
reg_param |
– |
正则化参数，默认为0.0 |
|
elastic_net_param |
– |
弹性网络参数，默认为0.0 |
|
tol |
– |
收敛阈值，默认为1e-6 |
|
fit_intercept |
– |
是否使用截距，默认为True |
|
standardization |
– |
是否对特征进行正则化，默认为True |
|
solver |
– |
优化时采用的处理算法，支持l-bfgs、normal、auto，默认为”auto” |
|
aggregation_depth |
– |
聚合深度，默认为2 |
|
loss |
– |
损失函数类型，支持squaredError、huber，默认为”squaredError” |
|
epsilon |
– |
默认为1.35 |
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_iter": 100, # @param {"label": "max_iter", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}
"reg_param": 0.0, # @param {"label": "reg_param", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}
"elastic_net_param": 0.0, # @param {"label": "elastic_net_param", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}
"tol": 1e-6, # @param {"label": "tol"", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}
"fit_intercept": True, # @param {"label": "fit_intercept"", "type": "boolean", "required": "true", "helpTip": ""}
"standardization": True, # @param {"label": "standardization"", "type": "boolean", "required": "true", "helpTip": ""}
"solver": "auto", # @param {"label": "solver", "type": "enum", "required": "true", "options": "l-bfgs,normal,auto", "helpTip": ""}
"aggregation_depth": 2, # @param {"label": "aggregation_depth", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}
"loss": "squaredError", # @param {"label": "loss", "type": "enum", "required": "true", "options": "squaredError,huber", "helpTip": ""}
"epsilon": 1.35 # @param {"label": "epsilon", "type": "number", "required": "true", "range": "(1.0,none)", "helpTip": ""}
}
linear_regression____id___ = MLSLinearRegression(**params)
linear_regression____id___.run()
# @output {"label":"pipeline_model","name":"linear_regression____id___.get_outputs()['output_port_1']","type":"PipelineModel"}
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