Data Science - Naive Bayes Work
Naive Bayes Algorithm :
Naive Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems.
It is mainly used in text classification that includes a high-dimensional training dataset.
Naive Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions.
It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.
Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles.
Each algorithm of Naive Bayes expects different types of data.
GaussianNB → When you have continuous features.
CategoricalNB → When you have categorical data.
MultinomialNB → Applied to text data.
This assignment will study following Question :
Prepare a classification model using Naive Bayes for salary data