朴素贝叶斯法(Naive Bayes)是基于贝叶斯定理与特征条件独立假设的分类方法。 工作原理是在已知样本特征的条件下,求出在各类别中发生的概率,然后取概率最大者为类别判断结果。 Naive Bayes 没有超参数可调,不能通过调参的办法提高准确率,只能依赖训练集样本的质量。 Naive Bayes 常用于文本分类中,比如垃圾邮件过滤。
java naive-bayes language-detection rapidminer Updated Mar 23, 2019 Java larsmoltsen / NaiveBayes Star 0 Code Issues Pull requests A simple, robust, multi-state Java implementation of the Naive Bayes classification algorithm. java classifier algorithm ai naive-bayes artificial-intelligence diagnos...
NaiveBayes classifier for JavaScript nodejsclassifiermachine-learningnaive-bayesmachine-learning-algorithmsjavascript-librarynaive-bayes-classifierbayesnaive-bayes-algorithmnaivebayesnaive-bayes-classificationnaivenode-ml UpdatedMar 7, 2023 JavaScript rudikershaw/whichx ...
machine-learning linear-regression machine-learning-algorithms python3 pytorch naive-bayes-classifier pca-analysis gaussian-mixture-models logistic-regression decision-trees ridge-regression naive-bayes-algorithm kmeans-clustering svm-classifier lasso-regression knn-classification pytorch-implementation tfidf-vectori...
Wrappers for discriminant analysis and naive Bayes models for use with the parsnip package - tidymodels/discrim
var classifier = bayes([options])Returns an instance of a Naive-Bayes Classifier.Pass in an optional options object to configure the instance. If you specify a tokenizer function in options, it will be used as the instance's tokenizer. It receives a (string) text argument - this is the ...
NaiveBayes classifier for JavaScript nodejsclassifiermachine-learningnaive-bayesmachine-learning-algorithmsjavascript-librarynaive-bayes-classifierbayesnaive-bayes-algorithmnaivebayesnaive-bayes-classificationnaivenode-ml UpdatedMar 7, 2023 JavaScript P7h/Spark-MLlib-Twitter-Sentiment-Analysis ...
model.py:包含NaiveBayes类的定义,用于训练和预测文本分类模型。 train.py:用于训练模型并将模型保存到文件model.pkl中。 main.py:用于加载模型,并读取test.txt中的邮件进行文本分类预测。 使用方法 1. 准备数据集 origin_data文件夹内有0和1两个子文件夹,每个子文件夹中可以包含多个文本文件,每个文件代表一个邮件...
Stanford NLP - Bayes Classifier #Improvements This classifier uses a very simple tokenizer which is just a module to split sentences into words. If your training set is large, you can rely on the available tokenizer, otherwise you need to have a better tokenizer specialized to the language of...
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