MODULE 2 S2.3.2 Flashcards
Naive Bayes
It is a supervised machine learning algorithm that uses principles of probability to perform classification tasks.
Naive Bayes classifier
It works by calculating the probability of an item belonging to a certain class based on its features.
Naive Bayes classifier
Naive Bayes classifiers are a collection of classification algorithms based on ___________.
Bayes’ Theorem
Naive Bayes is called naive because it assumes that each input variable is ______________.
independent
Naive Bayes is called _________ because it assumes that each input variable is independent.
naive
It ensures that how one feature affects an outcome in no way interacts with how another variable affects the same outcome.
Conditional independence
Naive Bayes Classifiers
Gaussian Naive Bayes
Multinomial Naive Bayes
Bernoulli Naive Bayes
Categorical Naive Bayes
Advantages of using Naive Bayes classifier for classification
It is very easy to implement and fast
Needs less training data
The classifier is not sensitive to irrelevant features
Applications of Naive Bayes
Text Classification
Spam Filtration
Sentiment Analysis
Classifying Articles
Recommendation Systems
Real-Time Predictions
T/F Naive Bayes assumes conditional independence where Bayes theorem does not.
True
T/F You should always include the denominator when calculating the most probable class.
False (it does not affect the outcome)
numerator
likelihood
prior probability
denominator
predictor prior probability
_______________ for each class is calculated and the class with maximum value is the final predicted class (result) of the model.
Posterior probabilities
Working of Naive Bayes Classifier
- Convert the given dataset into frequency tables.
- Generate Likelihood table by finding the probabilities of given features.
- Now, use Bayes theorem to calculate the posterior probability.
