What is the NaïveBayes Algorithm?
Naïve Bayes Algorithm is one of the popular classificationmachine learning algorithms and is included in supervised learning. that helps to classify the data based upon the conditional probability values computation. This algorithm is quite popular to be used in Natural Language Processingor NLP also real-time prediction, multi-class prediction, recommendation system, text classification, and sentiment analysis use cases. the algorithm is scalable and easy to implement for the large data set.
Thomas Bayes
The algorithm based on **Bayes theorem. **Bayes Theorem helps us to find the probability of a hypothesis given our prior knowledge.
Let’s look at the equation for Bayes Theorem,
Bayes Theorem
Naïve Bayes is a simple but surprisingly powerful predictive modeling algorithm. Naïve Bayes classifier calculates the probabilities for every factor. Then it selects the outcome with the highest probability.
Applications of Naïve Bayes Algorithm
1. Real-time prediction: Naïve Bayes Algorithm is fast and always ready to learn hence best suited for real-time predictions.
2. Multi-class prediction: The probability of multi-classes of any target variable can be predicted using a Naïve Bayes algorithm.
3. Text Classification where Naïve Bayes is mostly used is Spam Filtering in Emails (Naïve Bayes is widely used for text classification)
4. Text classification/ Sentiment Analysis/ Spam Filtering: Due to its better performance with multi-class problems and its independence rule, Naïve Bayes algorithm perform better or have a higher success rate in text classification, Therefore, it is used in Sentiment Analysis and Spam filtering.
5.**Recommendation System: **Naïve Bayes Classifier and Collaborative Filtering together build a Recommendation System that uses machine learning and data mining techniques to filter unseen information and predict whether a user would like a given resource or not.
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