Which of the following is the best algorithm for text classification?(1 Point)KNNDecision treeRandom forestNaive Bayes

Choosing the best algorithm for text classification depends on factors like the size of the dataset, the nature of the text, and the required accuracy. Here's a brief analysis of each option:

1. KNN (K-Nearest Neighbors):

   • Simple to understand but can be slow on large datasets. It works well for smaller datasets and is sensitive to the choice of distance metric.

2. Decision Tree:

   • Easy to interpret and visualize. However, they can be prone to overfitting, especially with complex datasets.

3. Random Forest:

   • An ensemble method that builds multiple decision trees and merges their results. It's generally robust and effective for many classification problems, including text classification. It handles overfitting better than single decision trees.

4. Naive Bayes:

   • Particularly well-suited for text classification tasks, especially for large datasets, due to its efficiency and effectiveness when dealing with high-dimensional data. It assumes features are independent, which often holds true in text data.

Conclusion:

Among the options provided, Naive Bayes is often considered the best algorithm for text classification, particularly for tasks like spam detection or sentiment analysis, due to its simplicity and good performance on text data.

Final Answer: Naive Bayes.