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

from sklearn.neighbors import KNeighborsClassifier

import numpy as np

def 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 SVC

def SVM(X,y,XX):

model = SVC(c=5.0)

model.fit(X,y)

predicted = model.predict(XX)

return predicted

SVM Classifier using cross validation

def svm_cross_validation(train_x, train_y):

from sklearn.grid_search import GridSearchCV

from sklearn.svm import SVC

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()

for para, val in list(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)

return model

LR

from sklearn.linear_model import LogisticRegression

def LR(X,y，XX):

model = LogisticRegression()

model.fit(X,y)

predicted = model.predict(XX)

return predicted

决策树(CART)

from sklearn.tree import DecisionTreeClassifier

def CTRA(X,y,XX):

model = DecisionTreeClassifier()

model.fit(X,y)

predicted = model.predict(XX)

return predicted

随机森林

from sklearn.ensemble import RandomForestClassifier

def CTRA(X,y,XX):

model = RandomForestClassifier()

model.fit(X,y)

predicted = model.predict(XX)

return predicted

GBDT(Gradient Boosting Decision Tree)

from sklearn.ensemble import GradientBoostingClassifier

def CTRA(X,y,XX):

model = GradientBoostingClassifier()

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，一个是基于伯努利分布求概率。

from sklearn.naive_bayes import GaussianNB

from sklearn.naive_bayes import MultinomialNB

from sklearn.naive_bayes import BernoulliNB

def GNB(X,y,XX):

model =GaussianNB()

model.fit(X,y)

predicted = model.predict(XX)

return predicted

def MNB(X,y,XX):

model = MultinomialNB()

model.fit(X,y)

predicted = model.predict(XX

return predicted

def BNB(X,y,XX):

model = BernoulliNB()

model.fit(X,y)

predicted = model.predict(XX

return predicted

希望本文所述对大家Python程序设计有所帮助。