Most Asked Machine Learning Interview Questions

Are Gaussian Naive Bayes the same as binomial Naive Bayes?

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Binomial Naive Bayes: It assumes that all our features are binary such that they take only two values. Means 0s can represent “word does not occur in the document” and 1s as “word occurs in the document”. Gaussian Naive Bayes: Because of the assumption of the normal distribution, Gaussian Naive Bayes is used in cases … Read more

What are the advantages of using a naive Bayes for classification?

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Very simple, easy to implement and fast. If the NB conditional independence assumption holds, then it will converge quicker than discriminative models like logistic regression. Even if the NB assumption doesn’t hold, it works great in practice. Need less training data. Highly scalable. It scales linearly with the number of predictors and data points. Can … Read more

How is linear classifier relevant to SVM?

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An svm is a type of linear classifier. If you don’t mess with kernels, it’s arguably the most simple type of linear classifier. Linear classifiers (all?) learn linear fictions from your data that map your input to scores like so: scores = Wx + b. Where W is a matrix of learned weights, b is … Read more

What is the difference between the normal soft margin SVM and SVM with a linear kernel?

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Hard-margin You have the basic SVM – hard margin. This assumes that data is very well behaved, and you can find a perfect classifier – which will have 0 error on train data. Soft-margin Data is usually not well behaved, so SVM hard margins may not have a solution at all. So we allow for … Read more

What is the difference between SVM Rank and SVR (Support Vector Regression)?

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One is used for ranking and the other is used for regression. There is a crucial difference between regression and ranking. In regression, the absolute value is crucial. A real number is predicted. In ranking, the only thing of concern is the ordering of a set of examples. We only want to know which example … Read more