"""fromsklearn.naive_bayesimportMultinomialNB# 使用sklearn中的贝叶斯分类器,并且加载贝叶斯分类器# 中的MultinomialNB多项式函数clf = MultinomialNB()# 加载多项式函数x_clf = clf.fit(X_train_tfidf, twenty_train.target)# 构造基于数据的分类器print(x_clf)# 分类器属性:MultinomialNB(alpha=1.0, class_prior...
可以创建一个管道,将TF–IDF 向量化方法与多项式朴素贝叶斯分类器组合在一起: from sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.pipeline import make_pipelinemodel = make_pipeline(TfidfVectorizer(), MultinomialNB()) 1. 通过这个管道,就可以将...
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 ...
from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() X_train = sc_X.fit_transform(X_train) X_test = sc_X.transform(X_test) # Fitting Classifier to the Training set from sklearn.naive_bayes import GaussianNB classifier = GaussianNB() classifier.fit(X_train, y_train) # ...
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]) ...
Naive Bayes Model Decision Boundaries. Image byauthor. (See section 5 for how this graph was made). Preface Just so you know what you are getting into, this is along storythat contains a mathematical explanation of the Naive Bayes classifier with 6 different Python examples. Please take a lo...
from sklearn.naive_bayes import GaussianNB #Calling the Class naive_bayes = GaussianNB() #Fitting the data to the classifier naive_bayes.fit(X_train , y_train) #Predict on test data y_predicted = naive_bayes.predict(X_test) The.fitmethod ofGaussianNBclass requires the feature data (X_trai...
print 'The accuracy of Naive Bayes Classifier is',mnb.score(X_test,y_test) print classification_report(y_test,y_pre) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 源码 2.sklearn.naive_bayes.GaussianNB ...
Python实现鸢尾花数据集分类问题——基于skearn的NaiveBayes 代码如下: #!/usr/bin/env python#encoding: utf-8__author__='Xiaolin Shen'fromsklearn.naive_bayesimportGaussianNB,BernoulliNBimportnumpy as npimportpandas as pdfromsklearnimportpreprocessingfromsklearnimportmodel_selectionimportmatplotlib.pyplot as ...
fromsklearn.model_selectionimportcross_val_scorefromsklearn.ensembleimportAdaBoostClassifierimportnumpyasnp# 导入numpy库importpandasaspdfromsklearnimporttree#导入sklearn的决策树模型(包括分类和回归两种)#画决策树pdf图 (DataFrame)defAdaboost(data):a=data.iloc[:,:-1]#特征矩阵b=data.iloc[:,-1]#目标...