Python使用sklearn库实现的各种分类算法简单应用小结

2019-11-25 12:31:21

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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 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

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

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（CART）

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

随机森林

from sklearn.ensemble import RandomForestClassifierdef 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 GradientBoostingClassifierdef 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 GaussianNBfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.naive_bayes import BernoulliNBdef 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 predicteddef BNB(X,y,XX):  model = BernoulliNB()  model.fit(X,y)  predicted = model.predict(XX  return predicted

更多关于Python相关内容感兴趣的读者可查看本站专题：《Python数据结构与算法教程》、《Python加密解密算法与技巧总结》、《Python编码操作技巧总结》、《Python函数使用技巧总结》、《Python字符串操作技巧汇总》及《Python入门与进阶经典教程》

希望本文所述对大家Python程序设计有所帮助。

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