Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes’ probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this first part of a series, we will take a look a...
Figure 4.35.Data mining process for naïve Bayes algorithm. Step 4: Execution and Interpretation The process shown inFig. 4.35has three result outputs: a model description, performance vector, and labeled dataset. The labeled dataset contains the test dataset with the predicted class as an added...
The Naive Bayes algorithm is one of the most popular and simple machine learning classification algorithms. It is based on the Bayes’ Theorem for calculating probabilities and conditional probabilities. You can use it for real-time and multi-class predictions, text classifications, spam filtering, ...
The types of problems that can be solved with NLP include machine translation, where given text in one language, the algorithm can translate the text to another language; semantic analysis; part of speech tagging; and document classification (of which spam filtering is an example). Research in ...
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[7] proposed a hybrid decision tree and a hybrid naive Bayes classification algorithm and solved the multi-classification problem. For text classification problems, Zhang et al. [8] created a two-layer Bayes model: random forest naive Bayes (RFNB); the first layer is a random forest model,...
We designed an experiment to testify the proposed algorithm. The experimental area is the downtown of Chengdu. The experimental subjects are two visitors who come to Chengdu. The experiment studies the algorithm in five aspects: (1) Naive Bayes classification results; (2) SAFS; (3) POI recomme...