Machine Learning Interview Questions | Data Science | Machine Learning

Machine Learning Interview Questions | Data Science | Machine Learning

With the growing demand for experts in the field of Machine Learning, more and more experts are starting to research common questions for their interviews. In this article, I’m going to introduce you to some very common machine learning interview questions that are collected by me and my other known machine learning experts who got these machine learning interview questions when they applied to jobs.

With the growing demand for experts in the field of Machine Learning, more and more experts are starting to research common questions for their interviews. In this article, I’m going to introduce you to some very common machine learning interview questions that are collected by me and my other known machine learning experts who got these machine learning interview questions when they applied to jobs.

Recently I wrote an article on how you can prepare a Data Science Resume if you are planning to give an interview then I will recommend you to follow those steps that I have shown in that article to make a good resume before diving into machine learning interview questions. It will help you in making a good impact to get the job and for a better career. You will find that article here.

Also, Read – Galaxy Classification Model with Machine Learning.

Common Machine Learning Interview Questions

If your model performs well on training data but generalizes poorly to new instances, what happens? Can you name three possible solutions?

If a model performs well on training data but generalizes poorly to new instances, the model is probably overfitting the training data (or we were very lucky on the training data). Possible solutions to overfitting are obtaining more data, simplifying the model (selecting a simpler algorithm, reducing the number of parameters or features used, or regularizing the model) or reducing noise in the data. training.

Suppose you are using polynomial regression. You draw the learning curves and you notice that there is a big gap between the learning error and the validation error. What is happening? What are three ways to solve this problem?

If the validation error is much higher than the training error, this is most likely due to your model over-fitting the training set. One way to try to solve this problem is to reduce the polynomial degree: a model with fewer degrees of freedom is less likely to overfit. Another thing you can try is to regularize the model – for example, adding a ℓ2 (Ridge) penalty or a ℓ1 (Lasso) penalty to the cost function. This will also reduce the degrees of freedom of the model. Finally, you can try increasing the size of the training set.

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