Python使用sklearn库实现的各种分类算法简单应用小结
作者：Yeoman92
这篇文章主要介绍了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 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
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