[Day_1]100-Days-Of-ML-Code

原文見https://github.com/MLEveryday/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.md

Day_1 warning解決方法：

第三步：處理丟失數據

from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

warning：

DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.
warnings.warn(msg, category=DeprecationWarning)

文檔：

sklearn.impute.SimpleImputer

sklearn.preprocessing.Imputer

 所以改為：

from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=np.nan, strategy="mean")
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3])

 

第4步：解析分類數據

創建虛擬變量

onehotencoder = OneHotEncoder(categorical_features=[0])
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

warning：

FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.
If you want the future behaviour and silence this warning, you can specify "categories='auto'".
In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.
warnings.warn(msg, FutureWarning)
DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20 and will be removed in 0.22. You can use the ColumnTransformer instead.
"use the ColumnTransformer instead.", DeprecationWarning)

所以改為：

onehotencoder = OneHotEncoder(categories='auto')
X = onehotencoder.fit_transform(X).toarray()
labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

 

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學習筆記后續更新。