Python使用sklearn库实现的各种分类算法简单应用小结

Python使用使用sklearn库实现的各种分类算法简单应用小结库实现的各种分类算法简单应用小结

主要介绍了Python使用sklearn库实现的各种分类算法,结合实例形式分析了Python使用sklearn库实现的KNN、

SVM、LR、决策树、随机森林等算法实现技巧,需要的朋友可以参考下

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

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)

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

return predicted

随机森林随机森林

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)