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

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 a test set (e.g., 80% training, 20% testing).

3. Train the Naive Bayes Model

  • Use the training data to train the Naive Bayes Classifier.

  • The model calculates:

    • Prior probabilities: The probability of each class P(C).

    • Likelihood probabilities: The probability of each word given a class P(W∣C).

4. Make Predictions

  • For a new document, the model calculates the posterior probability for each class P(C∣W) using Bayes' Theorem:

    P(C∣W)=P(W∣C)⋅P(C)P(W)

  • The class with the highest posterior probability is assigned to the document.

5. Evaluate the Model

  • Use the test set to evaluate the model's performance.

  • Common metrics include accuracy, precision, recall, and F1-score.

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