Naive Bayes algorithm In Machine Learning

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Naive Bayes algorithm In Machine Learning
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Table Of Content

1. What is Naive Bayes algorithm ?
2. Why is Naive Bayes algorithm Important?
3. How does Naive Bayes algorithm work?
4. Applications of Naive Bayes algorithm

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What is Naive Bayes algorithm ?
The Nave-Bayes algorithm is a supervised
learning algorithm that solves classification
problems and is built on the Bayes theorem. It
is primarily used in text classification with a
large training sample.
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Why is Naive Bayes algorithm Important?
It can work with both continuous and
discontinuous data. The number of predictors and
data points is highly scalable. It is fast and
can generate real-time predictions. It is not
affected by unimportant characteristics.
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How does Naive Bayes algorithm work?
The Naive Bayes Algorithm is an important
machine learning algorithm that helps in
classification issues. It is based on Bayes'
probability theory and is used for text
categorization, where high-dimensional datasets
are trained. Sentiment analysis, classifying new
pieces, and spam filtration are some of the most
useful applications of the Naive Bayes Algorithm.
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Applications of Naive Bayes algorithm

• Training Purposes.
• Make a Virtual Helper.
• Capability To Understand and Reply to a Wide
  Range Of Language Inputs.
• Capacity to Learn and Improve Over Time.
• Answer Inquiries.
• Improved Interpretability.
• Alternative to Google Search.
• Use AI to Write High-Performing Copy.
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