** Machine Learning Interview Questions:**

We’ve traditionally seen machine learning interview questions pop up

in several categories. The first really has to do with the algorithms

and 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.

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)

K-Nearest Neighbors is a supervised classification algorithm, while

k-means clustering is an unsupervised clustering algorithm. While the

mechanisms may seem similar at first, what this really means is that

in order for K-Nearest Neighbors to work, you need labeled data you

want to classify an unlabeled point into (thus the nearest neighbor

part). K-means clustering requires only a set of unlabeled points and

a threshold: the algorithm will take unlabeled points and gradually

learn how to cluster them into groups by computing the mean of the

distance between different points.

The critical difference here is that KNN needs labeled points and is

thus supervised learning, while k-means doesn’t — and is thus unsupervised

learning.

*Q4- Explain how a ROC curve works.*

More reading: Receiver operating characteristic (Wikipedia)

The ROC curve is a graphical representation of the contrast between

true positive rates and the false positive rate at various thresholds.

It’s often used as a proxy for the trade-off between the sensitivity of

the model (true positives) vs the fall-out or the probability it will trigger

a false alarm (false positives).

*Q5- Define precision and recall.*

More reading: Precision and recall (Wikipedia)

Recall is also known as the true positive rate: the amount of positives

your model claims compared to the actual number of positives there

are throughout the data. Precision is also known as the positive predictive

value, and it is a measure of the amount of accurate positives

your model claims compared to the number of positives it actually

claims. It can be easier to think of recall and precision in the context

of a case where you’ve predicted that there were 10 apples and 5

oranges in the case of 10 apples. You’d have perfect recall (there are

actually 10 apples, and you predicted there would be 10) but 66.7%

precision because out of the 15 events you predicted, only 10 (the

apples) are correct.

*Q6- What is Bayes’ Theorem? How is it useful in a machine*

*learning context?*

More reading: An Intuitive (and Short) Explanation of Bayes’ Theorem

(BetterExplained)

Bayes’ Theorem gives you the posterior probability of an event given

what is known as prior knowledge.

Mathematically, it’s expressed as the true positive rate of a condition

sample divided by the sum of the false positive rate of the population

and the true positive rate of a condition. Say you had a 60% chance of

actually having the flu after a flu test, but out of people who had the

flu, the test will be false 50% of the time, and the overall population

only has a 5% chance of having the flu. Would you actually have a

60% chance of having the flu after having a positive test?

Bayes’ Theorem says no. It says that you have a (.6 * 0.05) (True Positive

Rate of a Condition Sample) / (.6*0.05)(True Positive Rate of a

Condition Sample) + (.5*0.95) (False Positive Rate of a Population) =

0.0594 or 5.94% chance of getting a flu.

Bayes’ Theorem is the basis behind a branch of machine learning that

most notably includes the Naive Bayes classifier. That’s something

important to consider when you’re faced with machine learning interview

questions.

**Q7- Why is “Naive” Bayes naive?**

More reading: Why is “naive Bayes” naive? (Quora)

Despite its practical applications, especially in text mining, Naive

Bayes is considered “Naive” because it makes an assumption that is

virtually impossible to see in real-life data: the conditional probability

is calculated as the pure product of the individual probabilities of

components. This implies the absolute independence of features — a

condition probably never met in real life.

As a Quora commenter put it whimsically, a Naive Bayes classifier

that figured out that you liked pickles and ice cream would probably

naively recommend you a pickle ice cream.

*Q8- Explain the difference between L1 and L2 regularization.*

*More reading: What is the difference between L1 and L2 regularization?*

*(Quora)*

L2 regularization tends to spread error among all the terms, while L1

is more binary/sparse, with many variables either being assigned a 1

or 0 in weighting. L1 corresponds to setting a Laplacean prior on the

terms, while L2 corresponds to a Gaussian prior.

**Q9- What’s your favorite algorithm, and can you explain it**

**to me in less than a minute?**

This type of question tests your understanding of how to communicate

complex and technical nuances with poise and the ability to summarize

quickly and efficiently. Make sure you have a choice and make

sure you can explain different algorithms so simply and effectively

that a five-year-old could grasp the basics!

*Q10- What’s the difference between Type I and Type II*

*error?*

More reading: Type I and type II errors (Wikipedia)

Don’t think that this is a trick question! Many machine learning interview

questions will be an attempt to lob basic questions at you just to

make sure you’re on top of your game and you’ve prepared all of your

bases.

Type I error is a false positive, while Type II error is a false negative.

Briefly stated, Type I error means claiming something has happened

when it hasn’t, while Type II error means that you claim nothing is

happening when in fact something is.

A clever way to think about this is to think of Type I error as telling a

man he is pregnant, while Type II error means you tell a pregnant

woman she isn’t carrying a baby.

*Q11- What’s a Fourier transform?*

More reading: Fourier transform (Wikipedia)

A Fourier transform is a generic method to decompose generic functions

into a superposition of symmetric functions. Or as this more

intuitive tutorial puts it, given a smoothie, it’s how we find the recipe.

The Fourier transform finds the set of cycle speeds, amplitudes

and phases to match any time signal. A Fourier transform converts a

signal from time to frequency domain — it’s a very common way to

extract features from audio signals or other time series such as sensor

data.

**Q12- What’s the difference between probability and likelihood?**

More reading: What is the difference between “likelihood” and “probability”?

(Cross Validated)

*Q13- What is deep learning, and how does it contrast with*

other machine learning algorithms?

More reading: Deep learning (Wikipedia)

Deep learning is a subset of machine learning that is concerned with

neural networks: how to use backpropagation and certain principles

from neuroscience to more accurately model large sets of unlabelled

or semi-structured data. In that sense, deep learning represents an

unsupervised learning algorithm that learns representations of data

through the use of neural nets.

**Q14- What’s the difference between a generative and discriminative**

**model?**

More reading: What is the difference between a Generative and Discriminative

Algorithm? (Stack Overflow)

A generative model will learn categories of data while a discriminative

model will simply learn the distinction between different categories

of data. Discriminative models will generally outperform

generative models on classification tasks.

*Q15- What cross-validation technique would you use on a*

*time-series dataset?*

More reading: Using k-fold cross-validation for time-series model

selection (CrossValidated)

Instead of using standard k-folds cross-validation, you have to pay

attention to the fact that a time series is not randomly distributed

data — it is inherently ordered by chronological order. If a pattern

emerges in later time periods for example, your model may still pick

up on it even if that effect doesn’t hold in earlier years!

You’ll want to do something like forward chaining where you’ll be

able to model on past data then look at forward-facing data.

• fold 1 : training [1], test [2]

• fold 2 : training [1 2], test [3]

• fold 3 : training [1 2 3], test [4]

• fold 4 : training [1 2 3 4], test [5]

• fold 5 : training [1 2 3 4 5], test [6]

*Q16- How is a decision tree pruned?*

More reading: Pruning (decision trees)

Pruning is what happens in decision trees when branches that have

weak predictive power are removed in order to reduce the complexity

of the model and increase the predictive accuracy of a decision tree

model. Pruning can happen bottom-up and top-down, with

approaches such as reduced error pruning and cost complexity pruning.

Reduced error pruning is perhaps the simplest version: replace each

node. If it doesn’t decrease predictive accuracy, keep it pruned. While

simple, this heuristic actually comes pretty close to an approach that

would optimize for maximum accuracy.

*Q17- Which is more important to you– model accuracy, or*

*model performance?*

More reading: Accuracy paradox (Wikipedia)

This question tests your grasp of the nuances of machine learning

model performance! Machine learning interview questions often look

towards the details. There are models with higher accuracy that can

perform worse in predictive power — how does that make sense?

Well, it has everything to do with how model accuracy is only a subset

of model performance, and at that, a sometimes misleading one.

For example, if you wanted to detect fraud in a massive dataset with

a sample of millions, a more accurate model would most likely predict

no fraud at all if only a vast minority of cases were fraud. However,

this would be useless for a predictive model — a model designed to

find fraud that asserted there was no fraud at all! Questions like this

help you demonstrate that you understand model accuracy isn’t the

be-all and end-all of model performance.

*Q18- What’s the F1 score? How would you use it?*

*More reading: F1 score (Wikipedia)*

The F1 score is a measure of a model’s performance. It is a weighted

average of the precision and recall of a model, with results tending to

1 being the best, and those tending to 0 being the worst. You would

use it in classification tests where true negatives don’t matter much.

**Q19- How would you handle an imbalanced dataset?**

More reading: 8 Tactics to Combat Imbalanced Classes in Your

Machine Learning Dataset (Machine Learning Mastery)

An imbalanced dataset is when you have, for example, a classification

test and 90% of the data is in one class. That leads to problems: an

accuracy of 90% can be skewed if you have no predictive power on

the other category of data! Here are a few tactics to get over the

hump:

1- Collect more data to even the imbalances in the dataset.

2- Resample the dataset to correct for imbalances.

3- Try a different algorithm altogether on your dataset.

What’s important here is that you have a keen sense for what damage

an unbalanced dataset can cause, and how to balance that.

*Q20- When should you use classification over regression?*

More reading: Regression vs Classification (Math StackExchange)

Classification produces discrete values and dataset to strict categories,

while regression gives you continuous results that allow you to

better distinguish differences between individual points. You would

use classification over regression if you wanted your results to reflect

the belongingness of data points in your dataset to certain explicit

categories (ex: If you wanted to know whether a name was male or

female rather than just how correlated they were with male and

female names.)

*Q21- Name an example where ensemble techniques might*

*be useful.*

More reading: Ensemble learning (Wikipedia)

Ensemble techniques use a combination of learning algorithms to

optimize better predictive performance. They typically reduce overfitting

in models and make the model more robust (unlikely to be influenced

by small changes in the training data).

You could list some examples of ensemble methods, from bagging to

boosting to a “bucket of models” method and demonstrate how they

could increase predictive power.

*Q22- How do you ensure you’re not overfitting with a*

*model?*

More reading: How can I avoid overfitting? (Quora)

This is a simple restatement of a fundamental problem in machine

learning: the possibility of overfitting training data and carrying the

noise of that data through to the test set, thereby providing inaccurate

generalizations.

There are three main methods to avoid overfitting:

1- Keep the model simpler: reduce variance by taking into account

fewer variables and parameters, thereby removing some of the noise

in the training data.

2- Use cross-validation techniques such as k-folds cross-validation.

3- Use regularization techniques such as LASSO that penalize certain

model parameters if they’re likely to cause overfitting.

**Q23- What evaluation approaches would you work to**

gauge the effectiveness of a machine learning model?

More reading: How to Evaluate Machine Learning Algorithms (Machine

Learning Mastery)

You would first split the dataset into training and test sets, or perhaps

use cross-validation techniques to further segment the dataset

into composite sets of training and test sets within the data. You

should then implement a choice selection of performance metrics:

here is a fairly comprehensive list. You could use measures such as

the F1 score, the accuracy, and the confusion matrix. What’s

important here is to demonstrate that you understand the nuances of

how a model is measured and how to choose the right performance

measures for the right situations.

*Q24- How would you evaluate a logistic regression model?*

More reading: Evaluating a logistic regression (CrossValidated), Logistic

Regression in Plain English

A subsection of the question above. You have to demonstrate an

understanding of what the typical goals of a logistic regression are

(classification, prediction, etc.) and bring up a few examples and use

cases.

**Q25- What’s the “kernel trick” and how is it useful?**

More reading: Kernel method (Wikipedia)

The Kernel trick involves kernel functions that can enable in higherdimension

spaces without explicitly calculating the coordinates of

points within that dimension: instead, kernel functions compute the

inner products between the images of all pairs of data in a feature

space. This allows them the very useful attribute of calculating the

coordinates of higher dimensions while being computationally

cheaper than the explicit calculation of said coordinates. Many algorithms

can be expressed in terms of inner products. Using the kernel

trick enables us effectively run algorithms in a high-dimensional

space with lower-dimensional data.

(Learn about Springboard’s AI / Machine Learning

Bootcamp, the first of its kind to come with a job guarantee.)

Machine Learning Interview Questions:

Programming

These machine learning interview questions test your knowledge of

programming principles you need to implement machine learning

principles in practice. Machine learning interview questions tend to

be technical questions that test your logic and programming skills:

this section focuses more on the latter.

*Q26- How do you handle missing or corrupted data in a*

*dataset?*

More reading: Handling missing data (O’Reilly)

You could find missing/corrupted data in a dataset and either drop

those rows or columns, or decide to replace them with another value.

In Pandas, there are two very useful methods: isnull() and dropna()

that will help you find columns of data with missing or corrupted

data and drop those values. If you want to fill the invalid values with

a placeholder value (for example, 0), you could use the fillna()

method.

*Q27- Do you have experience with Spark or big data tools*

*for machine learning?*

More reading: 50 Top Open Source Tools for Big Data (Datamation)

You’ll want to get familiar with the meaning of big data for different

companies and the different tools they’ll want. Spark is the big data

tool most in demand now, able to handle immense datasets with

speed. Be honest if you don’t have experience with the tools

demanded, but also take a look at job descriptions and see what tools

pop up: you’ll want to invest in familiarizing yourself with them.

*Q28- Pick an algorithm. Write the psuedo-code for a parallel*

*implementation.*

More reading: Writing pseudocode for parallel programming (Stack

Overflow)

This kind of question demonstrates your ability to think in parallelism

and how you could handle concurrency in programming implementations

dealing with big data. Take a look at pseudocode

frameworks such as Peril-L and visualization tools such as Web

Sequence Diagrams to help you demonstrate your ability to write code

that reflects parallelism.

*Q29- What are some differences between a linked list and*

*an array?*

More reading: Array versus linked list (Stack Overflow)

An array is an ordered collection of objects. A linked list is a series of

objects with pointers that direct how to process them sequentially. An

array assumes that every element has the same size, unlike the linked

list. A linked list can more easily grow organically: an array has to be

pre-defined or re-defined for organic growth. Shuffling a linked list

involves changing which points direct where — meanwhile, shuffling

an array is more complex and takes more memory.

**Q30- Describe a hash table.**

More reading: Hash table (Wikipedia)

A hash table is a data structure that produces an associative array. A

key is mapped to certain values through the use of a hash function.

They are often used for tasks such as database indexing.

*Q31- Which data visualization libraries do you use? What*

*are your thoughts on the best data visualization tools?*

More reading: 31 Free Data Visualization Tools (Springboard)

What’s important here is to define your views on how to properly visualize

data and your personal preferences when it comes to tools.

Popular tools include R’s ggplot, Python’s seaborn and matplotlib, and

tools such as Plot.ly and Tableau.

Related : 20 Python Interview Questions

Machine Learning Interview Questions:

Company/Industry Specific

These machine learning interview questions deal with how to implement

your general machine learning knowledge to a specific company’s

requirements. You’ll be asked to create case studies and extend

your knowledge of the company and industry you’re applying for with

your machine learning skills.

*Q32- How would you implement a recommendation system*

*for our company’s users?*

More reading: How to Implement A Recommendation System? (Stack

Overflow)

A lot of machine learning interview questions of this type will involve

implementation of machine learning models to a company’s problems.

You’ll have to research the company and its industry in-depth, especially

the revenue drivers the company has, and the types of users the

company takes on in the context of the industry it’s in.

*Q33- How can we use your machine learning skills to generate*

*revenue?*

More reading: Startup Metrics for Startups (500 Startups)

This is a tricky question. The ideal answer would demonstrate

knowledge of what drives the business and how your skills could

relate. For example, if you were interviewing for music-streaming

startup Spotify, you could remark that your skills at developing a better

recommendation model would increase user retention, which

would then increase revenue in the long run.

The startup metrics Slideshare linked above will help you understand

exactly what performance indicators are important for startups and

tech companies as they think about revenue and growth.

*Q34- What do you think of our current data process?*

More reading: The Data Science Process Email Course – Springboard

This kind of question requires you to listen carefully and impart feedback

in a manner that is constructive and insightful. Your interviewer

is trying to gauge if you’d be a valuable member of their team and

whether you grasp the nuances of why certain things are set the way

they are in the company’s data process based on company- or industry-

specific conditions. They’re trying to see if you can be an intellectual

peer. Act accordingly.

Machine Learning Interview Questions: General

Machine Learning Interest

This series of machine learning interview questions attempts to gauge

your passion and interest in machine learning. The right answers will

serve as a testament for your commitment to being a lifelong learner

in machine learning.

*Q35- What are the last machine learning papers you’ve*

*read?*

More reading: What are some of the best research papers/books for

machine learning?

Keeping up with the latest scientific literature on machine learning is

a must if you want to demonstrate interest in a machine learning

position. This overview of deep learning in Nature by the scions of

deep learning themselves (from Hinton to Bengio to LeCun) can be a

good reference paper and an overview of what’s happening in deep

learning — and the kind of paper you might want to cite.

*Q36- Do you have research experience in machine learning?*

Related to the last point, most organizations hiring for machine learning

positions will look for your formal experience in the field.

Research papers, co-authored or supervised by leaders in the field,

can make the difference between you being hired and not. Make sure

you have a summary of your research experience and papers ready —

and an explanation for your background and lack of formal research

experience if you don’t.

*Q37- What are your favorite use cases of machine learning*

*models?*

More reading: What are the typical use cases for different machine

learning algorithms? (Quora)

The Quora thread above contains some examples, such as decision

trees that categorize people into different tiers of intelligence based

on IQ scores. Make sure that you have a few examples in mind and

describe what resonated with you. It’s important that you demonstrate

an interest in how machine learning is implemented.

*Q38- How would you approach the “Netflix Prize” competition?*

*More reading: Netflix Prize (Wikipedia)*

The Netflix Prize was a famed competition where Netflix offered

$1,000,000 for a better collaborative filtering algorithm. The team

that won called BellKor had a 10% improvement and used an ensemble

of different methods to win. Some familiarity with the case and its

solution will help demonstrate you’ve paid attention to machine

learning for a while.

*Q39- Where do you usually source datasets?*

More reading: 19 Free Public Data Sets For Your First Data Science

Project (Springboard)

Machine learning interview questions like these try to get at the heart

of your machine learning interest. Somebody who is truly passionate

about machine learning will have gone off and done side projects on

their own, and have a good idea of what great datasets are out there.

If you’re missing any, check out Quandl for economic and financial

data, and Kaggle’s Datasets collection for another great list.

*Q40- How do you think Google is training data for selfdriving*

*cars?*

More reading: Waymo Tech

Machine learning interview questions like this one really test your

knowledge of different machine learning methods, and your inventiveness

if you don’t know the answer. Google is currently using

Recaptcha to source labeled data on storefronts and traffic signs. They

are also building on training data collected by Sebastian Thrun at

GoogleX — some of which was obtained by his grad students driving

buggies on desert dunes!

*Q41- How would you simulate the approach AlphaGo took*

*to beat Lee Sidol at Go?*

More reading: Mastering the game of Go with deep neural networks

and tree search (Nature)

AlphaGo beating Lee Sidol, the best human player at Go, in a best-offive

series was a truly seminal event in the history of machine learning

and deep learning. The Nature paper above describes how this

was accomplished with “Monte-Carlo tree search with deep neural

networks that have been trained by supervised learning, from human

expert games, and by reinforcement learning from games of selfplay

## Comments

## Post a Comment

Leave a Message !