Most Asked Machine Learning Interview Questions

You came to know that your model is suffering from low bias and high variance. Which algorithm should you use to tackle it? Why?

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Low bias occurs when the model’s predicted values are near to actual values. In other words, the model becomes flexible enough to mimic the training data distribution. While it sounds like great achievement, but not to forget, a flexible model has no generalization capabilities. It means, when this model is tested on an unseen data, … Read more

You are assigned a new project which involves helping a food delivery company save more money. The problem is, company’s delivery team aren’t able to deliver food on time. As a result, their customers get unhappy. And, to keep them happy, they end up delivering food for free. Which machine learning algorithm can save them?

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You might have started hopping through the list of ML algorithms in your mind. But, wait! Such questions are asked to test your machine learning fundamentals. This is not a machine learning problem. This is a route optimization problem. A machine learning problem consist of three things: There exist a pattern. You cannot solve it … Read more

You are working on a time series data set. You manager has asked you to build a high accuracy model. You start with the decision tree algorithm, since you know it works fairly well on all kinds of data. Later, you tried a time series regression model and got higher accuracy than decision tree model. Can this happen? Why?

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Time series data is known to posses linearity. On the other hand, a decision tree algorithm is known to work best to detect non – linear interactions. The reason why decision tree failed to provide robust predictions because it couldn’t map the linear relationship as good as a regression model did. Therefore, we learned that, … Read more

Explain prior probability, likelihood and marginal likelihood in context of naiveBayes algorithm?

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Prior probability is nothing but, the proportion of dependent (binary) variable in the data set. It is the closest guess you can make about a class, without any further information. For example: In a data set, the dependent variable is binary (1 and 0). The proportion of 1 (spam) is 70% and 0 (not spam) … Read more

Why is naive Bayes so ‘naive’ ?

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naive Bayes is so ‘naive’ because it assumes that all of the features in a data set are equally important and independent. As we know, these assumption are rarely true in real world scenario. The term “naive” in “naive Bayes” refers to the assumption of independence among features in the dataset. It is considered “naive” … Read more