华为云AI开发平台ModelArts逻辑回归分类_云淘科技
概述
“逻辑回归”节点用于数据二分类，支持自动化建模。它可以根据输入训练集高效地完成参数自动调优，并通过LOGISTIC函数将线性回归的输出映射到[0,1]区间，最后根据阈值判断完成数据二分类。
逻辑回归本质上是一种线性分类方法，因此在考虑使用逻辑回归模型前，要保证所提出的特征与目标变量之间的关系可以使用线性模型来表达。特征与目标变量之间的线性关系越强，逻辑回归的模型性能越好。
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 |
|
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_col |
– |
算子输出的预测label对应的标签列，默认为”prediction_index” |
|
prediction_index_col |
– |
算子输出的预测label的列名，默认为”prediction” |
|
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 |
– |
迭代算法的收敛阈值，默认为1e-6 |
|
fit_intercept |
– |
是否要使用截距，默认为True |
|
standardization |
– |
是否正则化特征，默认为True |
|
aggregation_depth |
– |
聚合的深度，默认为2 |
|
family |
– |
模型训练中使用哪种标签分布，支持auto、binomial、multinomial，默认为”auto” |
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"}
"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_col": "prediction", # @param {"label": "prediction_col", "type": "string", "required": "true", "helpTip": ""}
"prediction_index_col": "prediction_index", # @param {"label": "prediction_index_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,none)", "helpTip": ""}
"elastic_net_param": 0.0, # @param {"label": "elastic_net_param", "type": "number", "required": "true", "range": "[0,none)", "helpTip": ""}
"tol": 1e-6, # @param {"label": "tol", "type": "number", "required": "true", "range": "(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": ""}
"aggregation_depth": 2, # @param {"label": "aggregation_depth", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}
"family": "auto", # @param {"label": "family", "type": "enum", "required": "true", "options":"auto,binomial,multinomial", "helpTip": ""}
"lower_bounds_on_coefficients": None,
"upper_bounds_on_coefficients": None,
"lower_bounds_on_intercepts": None,
"upper_bounds_on_intercepts": None
}
lr_classifier____id___ = MLSLogisticRegressionClassifier(**params)
lr_classifier____id___.run()
# @output {"label":"pipeline_model","name":"lr_classifier____id___.get_outputs()['output_port_1']","type":"PipelineModel"}
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