from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import VotingClassifier LR = LogisticRegression( solver='lbfgs', multi_class='multinomial' ) RF = RandomForestClassifier( n_estimators=...
(1)K最近邻 K-Nearest Neighbor (KNN) (2)朴素贝叶斯 Naive Bayes (3)决策树 Decision Tree - ID3 - C4.5 - 分类回归树 Classification And Regression Tree (CART) 区别:[决策树系列算法总结(ID3, C4.5, CART, Random Forest, GBDT)][1] (4)支持向量机器 Support Vector Machine (SVM) ### 2.1.2...
sklearn实现朴素贝叶斯 在sklearn 库中,基于贝叶斯定理的算法集中在 sklearn.naive_bayes 包中,根据对“似然度 P(xi|y)”计算方法的不同,我们将朴素贝叶斯大致分为三种: 多项式朴素贝叶斯(MultinomialNB) 伯努利分布朴素贝叶斯(BernoulliNB) 高斯分布朴素贝叶斯(GaussianNB) 另外一点要牢记,朴素贝叶斯算法的实现是基于假设...
from sklearn.naive_bayes import GaussianNB #载入数据集 X,y=load_iris(return_X_y=True) bayes_modle=GaussianNB() #训练数据 bayes_modle.fit(X,y) #使用模型进行分类预测 result=bayes_modle.predict(X) print(result) #对模型评分 model_score=bayes_modle.score(X,y) print(model_score) 输出结果: ...
sklearn.naive_bayes: Naive Bayes sklearn.neighbors: Nearest Neighbors sklearn.neural_network: Neural network models sklearn.calibration: Probability Calibration sklearn.cross_decomposition: Cross decomposition sklearn.pipeline: Pipeline sklearn.preprocessing: Preprocessing and Normalization ...
We will create a Naïve Bayes classifer that is composed of a feature vectorizer and the actual Bayes classifer. We will use theMultinomialNBclass from thesklearn.naive_bayesmodule. Scikitlearn has a very useful class calledPipeline(available in thesklearn.pipelinemodule) that eases the construc...
我们将使用sklearn.naive_bayes模块中的MultinomialNB类。为了用向量化器组成分类器,正如我们在第一章中看到的那样,scikit-learn 在sklearn.pipeline模块中有一个非常有用的类,称为Pipeline,可以简化复合分类器的构建,该分类器由几个向量化器和分类器组成。
Naive Bayes Decision Tree Random Forest Logistic Regression Support Vector Machine 新版Notebook- BML CodeLab上线,fork后可修改项目版本进行体验 sklearn之分类算法与手写数字识别 sklearn是Python的一个机器学习的库,它有比较完整的监督学习与非监督学习的模型。本文将使用sklearn库里的分类模型来对手写数字(MNIST)做...
3.4. Naive Bayes 4. Model selection and evaluation 4.1. Cross-validation: evaluating estimator performance 4.2. Grid Search: searching for estimator parameters 4.3. Pipeline: chaining estimators 4.4. Model evaluation: quantifying the quality of predictions ...
>>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) ... # doctest: +NORMALIZE_WHITESPACE Pipeline(memory=None, steps=[('standardscaler', ...