Python使用sklearn库实现的各种分类算法简单应用小结
本文实例讲述了Python使用sklearn库实现的各种分类算法简单应用。分享给大家供大家参考，具体如下：
KNN
fromsklearn.neighborsimportKNeighborsClassifier importnumpyasnp defKNN(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) returnpredicted
SVM
fromsklearn.svmimportSVC defSVM(X,y,XX): model=SVC(c=5.0) model.fit(X,y) predicted=model.predict(XX) returnpredicted
SVMClassifierusingcrossvalidation
defsvm_cross_validation(train_x,train_y):
fromsklearn.grid_searchimportGridSearchCV
fromsklearn.svmimportSVC
model=SVC(kernel='rbf',probability=True)
param_grid={'C':[1e-3,1e-2,1e-1,1,10,100,1000],'gamma':[0.001,0.0001]}
grid_search=GridSearchCV(model,param_grid,n_jobs=1,verbose=1)
grid_search.fit(train_x,train_y)
best_parameters=grid_search.best_estimator_.get_params()
forpara,valinlist(best_parameters.items()):
print(para,val)
model=SVC(kernel='rbf',C=best_parameters['C'],gamma=best_parameters['gamma'],probability=True)
model.fit(train_x,train_y)
returnmodel
LR
fromsklearn.linear_modelimportLogisticRegression defLR(X,y，XX): model=LogisticRegression() model.fit(X,y) predicted=model.predict(XX) returnpredicted
决策树（CART）
fromsklearn.treeimportDecisionTreeClassifier defCTRA(X,y,XX): model=DecisionTreeClassifier() model.fit(X,y) predicted=model.predict(XX) returnpredicted
随机森林
fromsklearn.ensembleimportRandomForestClassifier defCTRA(X,y,XX): model=RandomForestClassifier() model.fit(X,y) predicted=model.predict(XX) returnpredicted
GBDT(GradientBoostingDecisionTree)
fromsklearn.ensembleimportGradientBoostingClassifier defCTRA(X,y,XX): model=GradientBoostingClassifier() model.fit(X,y) predicted=model.predict(XX) returnpredicted
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，一个是基于伯努利分布求概率。
fromsklearn.naive_bayesimportGaussianNB fromsklearn.naive_bayesimportMultinomialNB fromsklearn.naive_bayesimportBernoulliNB defGNB(X,y,XX): model=GaussianNB() model.fit(X,y) predicted=model.predict(XX) returnpredicted defMNB(X,y,XX): model=MultinomialNB() model.fit(X,y) predicted=model.predict(XX returnpredicted defBNB(X,y,XX): model=BernoulliNB() model.fit(X,y) predicted=model.predict(XX returnpredicted
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