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)
标准化 （同模型使用方法相同）
from sklearn.preprocessing import StandardScaler
归一化（同模型使用方法相同）
from sklearn.preprocessing import MinMaxScaler
(模型参数待补充)
1.逻辑回归模型
Logistic函数图像很像一个“S”型，所以该函数又叫 sigmoid 函数。
from sklearn.liner_model import LogisticRegression
LR = LogisticRegression()
clf = LR.fit(X, y)
prediction = clf.predict(X)
sklearn.linear_model.LogisticRegression
2.线性判别（LDA）——费希尔判别
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.贝叶斯
from sklearn.naive_bayes import GaussianNB
sklearn.naive_bayes.GaussianNB
5.决策树
from sklearn.tree import DecisionTreeClassifier
sklearn.tree.DecisionTreeClassifier
6.支持向量机
from sklearn.svm import SVC
7.神经网络
from sklearn.neural_network import MLPClassifier