#对朴素贝叶斯分类器在新闻文本数据上的表现性能进行评估#从sklearn.metrics里导入classification_report用于详细的分类性能报告fromsklearn.metricsimportclassification_reportprint('The accuracy of Naive Bayes Classifier is',mnb.score(X_test,y_test))print(classification_report(y_test,y_predict,target_name=news....
sklearn.naive_bayes.GaussianNB 参考资料:https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html#sklearn.naive_bayes.GaussianNB 函数接口: gaussianNB.png 使用案例: importnumpyasnp X=np.array([[-1,-1],[-2,-1],[-3,-2],[1,1],[2,1],[3,2]])Y=np.array(...
from sklearn.naive_bayes import MultinomialNB from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer, CountVectorizer #nbc means naive bayes classifier nbc_1 = Pipeline([ (‘vect‘, CountVectorizer()), (‘clf‘, MultinomialNB()), ]) nbc...
然后通过使用fit()方法将模型拟合到数据集中的数据来训练模型。 # importing the module of the machine learning modelfromsklearn.naive_bayesimportGaussianNB# initializing the classifiergnb=GaussianNB()# training the classifiermodel=gnb.fit(train,train_labels) 训练完成后,我们可以使用训练好的模型对我们之前准...
Naive Bayes classifier calculates the probability of an event in the following steps: Step 1: Calculate the prior probability for given class labels Step 2: Find Likelihood probability with each attribute for each class Step 3: Put these value in Bayes Formula and calculate posterior probability. ...
naiveBayes.py源码,根据上述的算法理论自行实现,主要靠for循环一一实现,所以预测新样本的速度会较慢,这里只是为了加深对该算法的理解,速度优化问题(一般要选择更合适的数据结构)暂时先不讨论。 importnumpyasnp np.random.seed(0)classNaiveBayesClassifier:def__init__(self):# 属性(变量或方法)名以__开头的会被...
https://github.com/xitu/gold-miner/blob/master/TODO1/naive-bayes-classifier-sklearn-python-example-tips.md 用豆机实现的高斯分布 这篇教程详述了朴素贝叶斯分类器的算法、它的原理及优缺点,并提供了一个使用 Sklearn 库的示例。 背景 以著名的泰坦尼克号遇难者数据集为例。它收集了泰坦尼克号的乘客的个人信...
3.1 Multinomial Naive Bayes Classifier [python] ### #Multinomial Naive Bayes Classifier print'***nNaive Bayesn***' fromsklearn.naive_bayesimportMultinomialNB fromsklearnimportmetrics newsgroups_test = fetch_20newsgroups(subset ='test', categories = categories);...
Naive Bayes classifier for multinomial models(MultinomialNB,1.9.2) 参数: 1、alpha : float, optional (default=1.0),加性(Laplace/Lidstone)平滑参数(0不平滑)。 2、fit_prior : boolean, optional (default=True),是否要学习类别的先验概率。如果是False,将使用统一的先验概率。
naive_bayes import GaussianNB from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier as RFC from sklearn.tree import DecisionTreeClassifier as DTC from sklearn.linear_model import LogisticRegression as LR from sklearn.datasets import load_digits from sklearn.model_selection ...