About me

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Associate Professor

Anil Neerukonda Institute of Technology & Sciences

Sangivalsa, Visakhapatnam, A.P., INDIA.


VIDWAN | ORCID | SCOPUS | RESEARCH ID | GOOGLE ID

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A set of documents that need to be classified, use the Naive Bayesian Classifier

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The Naive Bayes Classifier is a probabilistic machine learning model widely used for classification tasks, including document classification. Based on Bayes' Theorem, it assumes that the features (in this case, words or terms in the documents) are conditionally independent given the class label. Despite this "naive" assumption, it often performs well in practice, especially for text classification. Steps to Perform Document Classification Using Naive Bayes 1.  Prepare the Dataset Documents : Assume you have a set of documents, each labeled with a category (e.g., "Sports", "Politics", "Technology"). Preprocessing : Tokenize the text into words. Remove stop words (e.g., "the", "is", "and"). Perform stemming or lemmatization to reduce words to their base forms. Convert text into a numerical representation, such as a  bag-of-words  or  TF-IDF  vector. 2.  Split the Dataset Divide the dataset into a  training set  and...
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