因此，我对此有疑问，一直在寻找答案。所以问题是我何时使用
from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() df = pd.DataFrame({'A':[1,2,3,7,9,15,16,1,5,6,2,4,8,9],'B':[15,12,10,11,8,14,17,20,4,12,4,5,17,19],'C':['Y','Y','Y','Y','N','N','N','Y','N','Y','N','N','Y','Y']}) df[['A','B']] = min_max_scaler.fit_transform(df[['A','B']]) df['C'] = df['C'].apply(lambda x: 0 if x.strip()=='N' else 1)
这之后，我将训练和测试模型（A，B作为特征，C如标签），并得到一些准确度得分。现在我的疑问是，当我必须预测新数据集的标签时会发生什么。说，
A
B
C
df = pd.DataFrame({'A':[25,67,24,76,23],'B':[2,54,22,75,19]})
因为当我规范化列时，A和的值B将根据新数据而不是将在其上训练模型的数据来更改。因此，现在将是下面的数据准备步骤之后的数据。
data[['A','B']] = min_max_scaler.fit_transform(data[['A','B']])
的价值A和B将关于改变Max和Min价值df[['A','B']]。的数据准备df[['A','B']]是关于Min Max的df[['A','B']]。
Max
Min
df[['A','B']]
Min Max
有关不同数字的数据准备如何有效相关？我不明白这个预测在这里如何正确。
MinMaxScaler
training
testing
综上所述：
scaler
TRAINING data
transform the TRAINING data
transformed training data
fit the predictive model
transform the TEST data
predict
trained model
transformed TEST data
使用数据的示例：
from sklearn import preprocessing min_max_scaler = preprocessing.MinMaxScaler() #training data df = pd.DataFrame({'A':[1,2,3,7,9,15,16,1,5,6,2,4,8,9],'B':[15,12,10,11,8,14,17,20,4,12,4,5,17,19],'C':['Y','Y','Y','Y','N','N','N','Y','N','Y','N','N','Y','Y']}) #fit and transform the training data and use them for the model training df[['A','B']] = min_max_scaler.fit_transform(df[['A','B']]) df['C'] = df['C'].apply(lambda x: 0 if x.strip()=='N' else 1) #fit the model model.fit(df['A','B']) #after the model training on the transformed training data define the testing data df_test df_test = pd.DataFrame({'A':[25,67,24,76,23],'B':[2,54,22,75,19]}) #before the prediction of the test data, ONLY APPLY the scaler on them df_test[['A','B']] = min_max_scaler.transform(df_test[['A','B']]) #test the model y_predicted_from_model = model.predict(df_test['A','B'])
使用虹膜数据的示例：
import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.svm import SVC data = datasets.load_iris() X = data.data y = data.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0) scaler = MinMaxScaler() X_train_scaled = scaler.fit_transform(X_train) model = SVC() model.fit(X_train_scaled, y_train) X_test_scaled = scaler.transform(X_test) y_pred = model.predict(X_test_scaled)
希望这可以帮助。
