I believe the issue is that fit_transform does not really store the mean and standard deviation. You need to first fit the transform using: scaler = preprocessing.StandardScaler(); scaler.fit(X) df_scaled = scaler.transform(X) # In your prediction step clf.predict(scaler.transform(query_df)...
Create scikit-learn'sPipelineobject and populate it with any pre-processing steps and the model object. fromsklearn.pipelineimportPipelinefromsklearn.treeimportDecisionTreeClassifierfromsklearn.preprocessingimportStandardScalerpipeline_obj=Pipeline([ ("scaler",StandardScaler()), ("model",DecisionTreeClassifier...
很多的 loss 函数都有 size_average 和 reduce 两个布尔类型的参数。因为一般损失函数都是直接计算 batch 的数据，因此返回的 loss 结果都是维度为 (batch_size, ) 的向量。
my_minmax_scaler=preprocessing.MinMaxScaler()my_minmax_scaler.fit(X[:,:3])my_minmax_scaler.transform(X[:,:3]).max(axis=0)array([1.,1.,1.]) It's very simple to change the minimum and maximum values of the MinMaxScaler class from its default of 0 and 1 , respectively: 从它默认的0...
参考sklearn的官方文档的数据预处理章节, 5.3. Preprocessing datascikit-learn.org 缩放过程可以分为以下几种: 缩放到均值为0,方差为1(Standardization——StandardScaler()) 缩放到0和1之间(Standardization——MinMaxScaler()) 缩放到-1和1之间(Standardization——MaxAbsScaler()) ...
X = dataset.iloc[:,1:2].values Y = dataset.iloc[:,-1].valuesfromsklearn.preprocessingimportStandardScaler sc_X = StandardScaler() sc_Y = StandardScaler() X = sc_X.fit_transform(X) Y = np.squeeze(sc_Y.fit_transform(Y.reshape(-1,1)))...