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What are some common machine learning interview questions?

Machine learning interview questions are an integral part of the data science interview and the path to becoming a data scientist, machine learning engineer or data engineer.

Eckovation created a free guide to data science interviews so we know exactly how they can trip candidates up!

In order to help resolve that, here is a curated and created a list of key questions that you could see in a machine learning interview.

You’ll be able to do well in any job interview with machine learning interview questions after reading through this piece.

You should always find this out prior to beginning your interview preparation Learn more about Machine Learning Note: A key to answer these questions is to have concrete practical understanding on ML and related statistical concepts.

(You are free to make practical assumptions.) Answer: Processing a high dimensional data on a limited memory machine is a strenuous task, your interviewer would be fully aware of that.

Not to forget, that’s the motive of doing PCA where, we aim to select fewer components (than features) which can explain the maximum variance in the data set.

By doing rotation, the relative location of the components doesn’t change, it only changes the actual coordinates of the points.

If we don’t rotate the components, the effect of PCA will diminish and we’ll have to select more number of components to explain variance in the data set.

We know, in a normal distribution, ~68% of the data lies in 1 standard deviation from mean (or mode, median), which leaves ~32% of the data unaffected.

In an imbalanced data set, accuracy should not be used as a measure of performance because 96% (as given) might only be predicting majority class correctly, but our class of interest is minority class (4%) which is the people who actually got diagnosed with cancer.

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, a linear regression model can provide robust prediction given the data set satisfies its linearity assumptions.

A machine learning problem consist of three things: Always look for these three factors to decide if machine learning is a tool to solve a particular problem.

Discarding correlated variables have a substantial effect on PCA because, in presence of correlated variables, the variance explained by a particular component gets inflated.

If you run PCA on this data set, the first principal component would exhibit twice the variance than it would exhibit with uncorrelated variables.

Answer: As we know, ensemble learners are based on the idea of combining weak learners to create strong learners.

For example: If model 1 has classified User1122 as 1, there are high chances model 2 and model 3 would have done the same, even if its actual value is 0.

Therefore, ensemble learners are built on the premise of combining weak uncorrelated models to obtain better predictions Q12.

kmeans algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close to each other.

kNN algorithm tries to classify an unlabeled observation based on its k (can be any number ) surrounding neighbors.

The formula of R² = 1 – ∑(y – y´)²/∑(y – ymean)² where y´ is predicted value.

In absence of intercept term (ymean), the model can make no such evaluation, with large denominator, ∑(y - y´)²/∑(y)² equation’s value becomes smaller than actual, resulting in higher R².

VIF value <= 4 suggests no multicollinearity whereas a value of >= 10 implies serious multicollinearity.

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