Machine learning (ML) job interviews typically test a mix of foundational theory, algorithmic knowledge, practical implementation (data preprocessing), and system design.
7/30/2017 41 Key Machine Learning Interview Questions with Answers
https://www.springboard.com/blog/machine-learning-interview-questions/ 1/23
JAN 9, 2017
Roger Huang 0 DATA SCIENCE
41 Essential Machine Learning
Interview Questions (with answers)
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. Springboard 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. There are some answers to go along with them so you don’t get stumped.
You’ll be able to do well in any job interview with machine learning interview
questions after reading through this piece.
Machine Learning Interview Questions:
Categories
We’ve traditionally seen machine learning interview questions pop up in
several categories. The first really has to do with the algorithms and
7/30/2017 41 Key Machine Learning Interview Questions with Answers
https://www.springboard.com/blog/machine-learning-interview-questions/ 2/23
theory behind machine learning. You’ll have to show an understanding
of how algorithms compare with one another and how to measure their
efficacy and accuracy in the right way. The second category has to do
with your programming skills and your ability to execute on top of those
algorithms and the theory. The third has to do with your general interest
in machine learning: you’ll be asked about what’s going on in the
industry and how you keep up with the latest machine learning trends.
Finally, there are company or industry-specific questions that test your
ability to take your general machine learning knowledge and turn it into
actionable points to drive the bottom line forward.
We’ve divided this guide to machine learning interview questions into
the categories we mentioned above so that you can more easily get to
the information you need when it comes to machine learning interview
questions.
Machine Learning Interview Questions:
Algorithms/Theory
These algorithms questions will test your grasp of the theory behind
machine learning.
Q1- What’s the trade-off between bias and variance?
More reading: Bias-Variance Tradeoff (Wikipedia)
Bias is error due to erroneous or overly simplistic assumptions in the
learning algorithm you’re using. This can lead to the
model underfitting your data, making it hard for it to have high predictive
accuracy and for you to generalize your knowledge from the training set
to the test set.
7/30/2017 41 Key Machine Learning Interview Questions with Answers
https://www.springboard.com/blog/machine-learning-interview-questions/ 3/23
Variance is error due to too much complexity in the learning algorithm
you’re using. This leads to the algorithm being highly sensitive to high
degrees of variation in your training data, which can lead your model
to overfit the data. You’ll be carrying too much noise from your training
data for your model to be very useful for your test data.
The bias-variance decomposition essentially decomposes the learning
error from any algorithm by adding the bias, the variance and a bit of
irreducible error due to noise in the underlying dataset. Essentially, if
you make the model more complex and add more variables, you’ll lose
bias but gain some variance — in order to get the optimally reduced
amount of error, you’ll have to tradeoff bias and variance. You don’t
want either high bias or high variance in your model.
Q2- What is the difference between supervised and unsupervised
machine learning?
More reading: What is the difference between supervised and
unsupervised machine learning? (Quora)
Supervised learning requires training labeled data. For example, in
order to do classification (a supervised learning task), you’ll need to first
label the data you’ll use to train the model to classify data into your
labeled groups. Unsupervised learning, in contrast, does not require
labeling data explicitly.
Q3- How is KNN different from k-means clustering?
More reading: How is the k-nearest neighbor algorithm different from k-
means clustering? (Quora)
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