from sklearn.model_selection import train_test_split
eg: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
標准化 （同模型使用方法相同）
from sklearn.preprocessing import StandardScaler
歸一化（同模型使用方法相同）
from sklearn.preprocessing import MinMaxScaler
(模型參數待補充)
1.邏輯回歸模型
Logistic函數圖像很像一個“S”型，所以該函數又叫 sigmoid 函數。
from sklearn.liner_model import LogisticRegression
LR = LogisticRegression()
clf = LR.fit(X, y)
prediction = clf.predict(X)
sklearn.linear_model.LogisticRegression
2.線性判別（LDA）——費希爾判別
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
LDA = LinearDiscriminantAnalysis()
clf = LDA.fit(X, y)
prediction = clf.predict(X)
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
3.KNN
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier().fit(X, y) _可以一步到位
prediction = clf.predict(X)
sklearn.neighbors.KNeighborsClassifier
4.貝葉斯
from sklearn.naive_bayes import GaussianNB
sklearn.naive_bayes.GaussianNB
5.決策樹
from sklearn.tree import DecisionTreeClassifier
sklearn.tree.DecisionTreeClassifier
6.支持向量機
from sklearn.svm import SVC
7.神經網絡
from sklearn.neural_network import MLPClassifier