或者,您可以删除异常值并使用上述 2 个缩放器中的任何一个(选择取决于数据是否呈正态分布) 补充说明:如果在train_test_split之前使用scaler,会发生数据泄露。在 train_test_split 之后使用缩放器 原文由 perpetualstudent 发布,翻译遵循 CC BY-SA 4.0 许可协议 有用 回复 撰写回答 你尚未登录,登录后可以 和开发...
Option 1: scaler = StandardScaler() Train_norm = scaler.fit_transform(X_train) Test_norm = scaler.fit_transform(X_test) y_train_norm = scaler.fit_transform(y_train) y_test_norm = scaler.fit_transform(y_test) Option 2: scaler_x = StandardScaler() scaler_y = StandardScaler() Train_nor...
df_scaled = scaler.transform(X) # In your prediction step clf.predict(scaler.transform(query_df)) I've verified that the SimpleScaler transform actually works fine (see:https://gallery.cortanaanalytics.com/Experiment/b3873a36433e46b7b6ff6228bf9ee302). Could you please try the steps above to...
fromsklearn.pipelineimportPipelinefromsklearn.treeimportDecisionTreeClassifierfromsklearn.preprocessingimportStandardScalerpipeline_obj=Pipeline([ ("scaler",StandardScaler()), ("model",DecisionTreeClassifier()) ]) CallPipeline.fit(X,y)method to train the model. ...
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代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 my_odd_scaler=preprocessing.MinMaxScaler(feature_range=(-3.14,3.14)) Furthermore, another option is normalization. This will scale each sample to have a length of 1. This is different from the other types of scaling done previously, where...
(iris_df_lintrans.values, i) for i in range(len(iris_df_lintrans.columns))] corrmat_lintrans = pd.DataFrame(round(iris_df_lintrans.corr(), 4)) ### Applying workhorse sklearn scalers, except nonlinear ones from sklearn.preprocessing import StandardScaler, MinMaxScaler scaler = MinMaxScaler...
我使用sklearn的StandardScaler()来规范我的数据,所以我只想将mean_和var_属性从scaler保存到model,保存模型,并且当我重新加载模型时可以访问这些属性。目前,当我重新加载模型时,我添加的属性并不存在。正确的做法是什么?代码: # Normalize data scaler = StandardScaler() scaler.fit(X_train) ... # Create ...
Standard scaler 和 MinMaxScaler 之间的区别 社区维基1 发布于 2022-11-15 新手上路,请多包涵 MinMaxScaler() 和StandardScaler() 之间有什么区别。 mms = MinMaxScaler(feature_range = (0, 1)) (用于机器学习模型) sc = StandardScaler() (在另一个机器学习模型中,他们使用标准缩放器而不是最小最大缩放器)...