各分类算法：

KNN

from sklearn.neighbors import KNeighborsClassifierimport numpy as npdef KNN(X,y,XX)：#X,y 分别为训练数据集的数据和标签，XX为测试数据    model = KNeighborsClassifier(n_neighbors=10)#默认为5    model.fit(X,y)    predicted = model.predict(XX)    return predicted

SVM

from sklearn.svm import SVCdef SVM(X,y,XX):        model = SVC(c=5.0)    model.fit(X,y)    predicted = model.predict(XX)    return predicted

LR

from sklearn.linear_model import LogisticRegressiondef LR(X,y，XX):        model = LogisticRegression()    model.fit(X,y)    predicted = model.predict(XX)    return predicted

决策树

from sklearn.tree import DecisionTreeClassifierdef CTRA(X,y,XX):    model = DecisionTreeClassifier()    model.fit(X,y)    predicted = model.predict(XX)    return predicted

朴素贝叶斯：一个是基于高斯分布求概率，一个是基于多项式分布求概率。

from sklearn.naive_bayes import GaussianNBfrom sklearn.naive_bayes import MultinomialNBdef GNB(X,y,XX):        model =GaussianNB()    model.fit(X,y)        predicted = model.predict(XX)    return predicteddef MNB(X,y,XX):        model = MultinomialNB()    model.fit(X,y)    predicted = model.predict(XX    return predicted