Multiple choices questions in Machine learning. Interview questions on machine learning, quiz questions for data scientist answers explained, Exam questions in machine learning, difference between generative and discriminative ML models, why naive bayes a generative model?
Machine Learning MCQ - Why Naive Bayes classifier is a generative model?
1. Which is true
about generative and discriminative models?
a) Generative
models model the joint distribution P(class = C AND sample = x)
b) Perceptron is a
generative model
c) Logistic
regression is a generative model
d) The naive Bayes
classifier is a generative model
|
Answer: (a) Generative models model the joint distribution
P(class = C AND sample = x) and (d) The
naive Bayes classifier is a generative model (a) Generative models model the joint
distribution The joint distribution P(X,Y) is the probability
distribution that describes how the features X and the labels Y are
distributed together. It gives us the probability of observing a particular
combination of features X and the associated class label Y simultaneously. The generative models are those that model the joint
distribution because they aim to describe the probabilistic relationship
between both the input data (features) and the output label (class),
together. In other words, generative models try to understand how the data is
generated in the context of both the features and the labels.
(d) Naïve Bayes is generative model Naive Bayes is generative because it models how the
data (features and labels) are generated. Specifically, it models the joint
probability P(X,Y) by assuming a probabilistic process where the features are
generated given the class label. It then uses Bayes' theorem to compute the
posterior probability of the class, making it a generative model that focuses
on how the data comes together. This contrasts with discriminative models,
which directly model the decision boundary without modeling how the data is
generated. Suppose that you are using Naïve Bayes to classify
emails as ‘spam’ or ‘not spam’. In this classification, the Naive Bayes model
has "generated" a probabilistic model of the features (words)
conditioned on the class (spam or not spam). That is it calculates the
likelihood of observing words given the class [P(word|spam)
and P(word|no spam)]. This is why it’s considered generative.
Logistic regression is a discriminative model which focuses on directly
modeling the decision boundary of classes based on estimating the conditional
probability which is P(Y|X). Perceptron is a discriminative model. |
Related links:
Why naive bayes is a generative classifier?
Generative vs discriminative machine learning algorithm
Why perceptron is a discriminative classifier?
Why logistic regression is a discriminative classifier?
generative model uses joint probability distribution whereas discriminative model uses the conditional probability distribution
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