小猿圈Python人工智能之使用sklearn库实现各种分类算法
人工智能发展的今天，现在很多企业也都在学习python技术开发，但是真正会的却不是很多，那么对于小白的话该如何学习python呢？下面小猿圈Python讲师为你讲解使用sklearn库实现各种分类算法，希望对于学习python开发的你有一定的帮助。https://upload-images.jianshu.io/upload_images/18616261-53fc991fd919abf4.jpg?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240KNNfrom 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5model.fit(X,y)predicted = model.predict(XX)return predictedSVMfrom sklearn.svm import SVCdef SVM(X,y,XX):model = SVC(c=5.0)model.fit(X,y)predicted = model.predict(XX)return predictedSVM Classifier using cross validationdef svm_cross_validation(train_x, train_y):from sklearn.grid_search import GridSearchCVfrom sklearn.svm import SVCmodel = SVC(kernel='rbf', probability=True)param_grid = {'C': , 'gamma': }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 modelLRfrom 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 predictedGBDT (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(XXreturn predicteddef BNB(X,y,XX):model = BernoulliNB()model.fit(X,y)predicted = model.predict(XXreturn predicted以上就是小猿圈Python讲师对于使用sklearn库实现各种分类算法的介绍了，相信你有了一定的了解，那么赶快去做吧，记住学习是一门需要坚持的Python交流群：874680195，如果遇到问题可以到小猿圈官网找答案的，里面有最新最全面的课程。
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