Naive Bayes

Naïve Bayes Classifier Algorithm

• Naïve 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.
• Naïve 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.

# Mathematics in Bayes

# Coding

# converting the data into numerical data

# converting into numpy array

# Training and testing

# types of mushroom

# p type and e type in the dataframe

# # Building our Naive Bayes classifier

We need a function which will compute the prior probability for each of the classes present in the dataset

# hint

# hint

# conditional probability

# hint

# computer posterior probability for each test example and make prediction