(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
sklearn.neural_network.MLPClassifier
原文：https://www.cnblogs.com/myra-dream/p/13797410.html