Table of Contents
What is the purpose of the independence assumption for the naive Bayes classifier?
It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
What are the disadvantages of naive Bayesian classifier?
Disadvantages
- Naive Bayes assumes that all predictors (or features) are independent, rarely happening in real life.
- This algorithm faces the ‘zero-frequency problem’ where it assigns zero probability to a categorical variable whose category in the test data set wasn’t available in the training dataset.
When can a feature independence assumption be reasonable and when not?
Assignment 3. Run test_bayes_iris() and test_bayes_vowels() . Q1: When can a feature independence assumption be reasonable and when not? A1: It is reasonable when the features are conditionally independent given classification, or at least reasonably independent, still works with little dependence.
What is the naive assumption in a naive Bayes classifier Mcq?
The fundamental Naive Bayes assumption is that each feature makes an: independent. equal.
How does naive Bayes classification work?
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class. The class with the highest probability is considered as the most likely class.
What are the pros and cons of Naive Bayes classifier?
Pros and Cons of Naive Bayes Algorithm
- The assumption that all features are independent makes naive bayes algorithm very fast compared to complicated algorithms. In some cases, speed is preferred over higher accuracy.
- It works well with high-dimensional data such as text classification, email spam detection.
What are the advantages of the Assumption in naive Bayes?
Advantages. It is easy and fast to predict the class of the test data set. It also performs well in multi-class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
What makes naive Bayes naive?
Naive Bayes is a simple and powerful algorithm for predictive modeling. Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.
Why is Naive Bayes called naive Mcq?
The Naïve Bayes algorithm is comprised of two words Naïve and Bayes, Which can be described as: Naïve: It is called Naïve because it assumes that the occurrence of a certain feature is independent of the occurrence of other features.
What is naive Bayes assumption?
Naive Bayes Assumption: P(→x | y) = d ∏ α = 1P(xα | y), where xα = [→x]α is the value for feature α i.e., feature values are independent given the label! This is a very bold assumption. For example, a setting where the Naive Bayes classifier is often used is spam filtering. Here, the data is emails and the label is spam or not-spam.
What is an example of a naive Bayes classifier?
For example, a setting where the Naive Bayes classifier is often used is spam filtering. Here, the data is emails and the label is spam or not-spam. The Naive Bayes assumption implies that the words in an email are conditionally independent, given that you know that an email is spam or not.
Does naive Bayes lead to a linear decision boundary?
Naive Bayes leads to a linear decision boundary in many common cases. Illustrated here is the case where P(xα | y) is Gaussian and where σα, c is identical for all c (but can differ across dimensions α). The boundary of the ellipsoids indicate regions of equal probabilities P(→x | y).
How do you find the naive Bayes probability?
To calculate the Naive Bayes probability, P ( d | c ) x P ( c ), we calculate P ( xi | c ) for each xi in d, and multiply them together. Then we multiply the result by P ( c ) for the current class. We do this for each of our classes, and choose the class that has the maximum overall value.