具体参考:sklearn.naive_bayes.MultinomialNB - scikit-learn 0.19.0 中文文档 - ApacheCN 3 .伯努利朴素贝叶斯: BernoulliNB实现了用于多重伯努利分布数据的朴素贝叶斯训练和分类算法,即有多个特征,但每个特征 都假设是一个二元 (Bernoulli, boolean) 变量。 具体参考:sklearn.naive_bayes.BernoulliNB - scikit-learn ...
highlight=bayes#module-sklearn.naive_bayes 高斯贝叶斯接口面向连续性特征 -- 例如鸢尾花分类 Category接口面向离散型特征 贝努力接口面向真假型特征 Thesklearn.naive_bayesmodule implements Naive Bayes algorithms. These are supervised learning methods based on applying Bayes’ theorem with strong (naive) featu...
from sklearn import datasets, model_selection,naive_bayes dic1 = datasets.load_wine() xtrain, xtest, ytrain, ytest = model_selection.train_test_split(dic1.data, dic1.target, test_size=0.3,random_state=1) m1 = naive_bayes.GaussianNB().fit(xtrain,ytrain) #高斯分布(正态分布) #m2 =...
1.sklearn.naive_bayes.MultinomialNB 多项式朴素贝叶斯(Multinomial Naive Bayes),即所有特征都是离散型的随机变量(例如在做文本分类时所使用的词向量就是离散型的).在sklearn中,这个方法的名称为MultinaomialNB.其相关信息如下: 注:在sklearn中,计算先验概率时并没有加入平滑项 示例 import textProc...
from sklearn.naive_bayes import GaussianNB # Build a Gaussian Classifier model = GaussianNB() # Model training model.fit(X_train, y_train) # Predict Output predicted = model.predict([X_test[6]]) print("Actual Value:", y_test[6]) print("Predicted Value:", predicted[0]) Powered By ...
跟着Leo机器学习:sklearn之Naive Bayes 一个很有趣的个人博客,不信你来撩 fangzengye.com sklearn 框架 函数导图 1.9. Naive Bayes 1.9.1. Gaussian Naive Bayes fromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.naive_bayesimportGaussianNB...
fromsklearnimportnaive_bayes#导入贝叶斯模型 fromsklearnimportmetrics fromsklearn.datasetsimportload_iris#导入鸢尾花数据集 fromsklearn.metricsimportconfusion_matrix fromsklearn.metricsimportprecision_score fromsklearn.metricsimportrecall_score fromsklearn.metricsimportf1_score ...
一、SKLearn各个模块 (一)监督学习的各个模块 1、neighbors近邻算法 2、svm支持向量机算法 3、kernal_ridge核岭回归 4、neighbors近邻算法 5、discriminant_analysis判别分析 6、linear_model广义线性模型 7、ensemble集成方法 8、tree决策树 9、naive_bayes朴素贝叶斯 10 ...
# 需要导入模块: from sklearn import naive_bayes [as 别名]# 或者: from sklearn.naive_bayes importGaussianNB[as 别名]deftest_22_gaussian_nb(self):print("\ntest 22 (GaussianNBwithout preprocessing) [binary-class]\n") X, X_test, y, features, target, test_file = self.data_utility.get_da...
from sklearn.naive_bayes import GaussianNB # Build a Gaussian Classifier model = GaussianNB() # Model training model.fit(X_train, y_train) # Predict Output predicted = model.predict([X_test[6]]) print("Actual Value:", y_test[6]) ...