Want to crack the machine learning algorithm interview? **Here’s the secret:** Read and memorize.

Machine learning algorithm interview questions aren’t so much a test of your technical skills, but rather, they assess your ability to study and memorize concepts. You will do well if you know:

- the
**definition of an algorithm**, - can
**clearly explain how it works**, - and
**have a strong grasp of the mathematical formulas that support it**.

To help you practice, we’ve highlighted common machine learning algorithm question topics and have provided example algorithm interview questions to help you study.

Machine learning algorithm interviews tend to feel more like a discussion.

For example, the interviewer might ask you for a definition of a particular algorithm, and then follow up with more detailed questions about that algorithm, e.g., pros or cons, or what’s going on under the hood.

Therefore, beyond a basic definition and explanation of an algorithm, be sure you have in-depth knowledge of its optimization speed, performance requirements, and use cases. Algorithm interviews tend to start with the basics, and progress into technical discussions about the finer points of a particular algorithm or algorithms.

The most common frameworks for these questions include:

**Definitions -**Definition-based questions that dive into a particular machine learning algorithm, e.g.*“provide a high-level overview of linear regression.”***Algorithm explanation -**A deep-dive into what’s working under the hood of a particular algorithm. Here it’s helpful to illustrate the example with use cases.**Comparisons -**An explanation of differences between two different algorithms. One tip: Provide a use case in which one algorithm would be preferred over the other.**Assumptions -**These are process questions that explore the assumptions and predispositions that must be in place before applying models to a dataset.**Tuning and Parameters -**These are questions about hyper-parameter tuning differences between each machine learning technique. You should memorize the parameters and understand how you would tune them for the most common algorithms.

Here are some algorithm questions examples from Interview Query to help you practice:

With a question asking about the assumptions of linear regression, know that there are several assumptions that are baked into the dataset and how the model is built. The first assumption is that there is a **linear relationship between the features and the response variable**, otherwise known as the value you’re trying to predict.

A random forest is a supervised learning algorithm, which is essentially a “forest” of decision trees trained with the bagging method. In general, as the number of trees increases, the accuracy of the model increases.

Boolean variables are variables that have a value of 0 or 1. Examples of these types of variables include things like gender, whether someone is employed or not, or whether something is gray or white.

The sign of the coefficient is important. If you have a positive sign on the coefficient, then that means, all else equal, the **variable has a higher likelihood of having a positive influence on your outcome variable**. Conversely, a negative sign implies an inverse relationship between the variable and the outcome you are interested in.

Multicollinearity in a regression model describes a situation in which two or more independent variables are highly correlated with one another. There are many indicators you can use to detect multicollinearity.

For example, when standard errors are orders of magnitude higher than coefficients, that’s usually a strong indicator.

With multicollinearity in regression questions, start by breaking down the problem.

Multiple linear regression is a method that uses **several independent variables to predict or explain the dependent variable** we are interested in. When using this technique, we assume that the independent or explanatory variables are also independent of one another (i.e., the values do not affect one another).

Multicollinearity occurs when different independent variables are correlated, and if the correlation between variables is high enough, this can cause problems in fitting the linear regression model and in your post-analysis.

OK, now, how would you go about tackling multicollinearity?

With a bagging technique like the random forest, we have several base learners or decision trees which are generated in parallel and form the base learners of the bagging technique.

However, in boosting, the trees are built sequentially such that each subsequent tree aims to reduce the errors of the previous tree. Each tree learns from its predecessors and updates the residual errors. Hence, the tree that grows next in the sequence will learn from an updated version of the residuals.

k-Means is a clustering algorithm that clusters a set of points N into k clusters. The k is chosen by the model developer. Once the algorithm finishes running, each observation will be assigned to one cluster.

With any specification of k, the algorithm will eventually converge; that is, no more updates will be possible and each observation will be assigned to a cluster.

Using logic, sketch out a proof that a k-Means clustering algorithm will converge in a finite number of steps. Note that the proof is not necessarily for the most efficient or effective real-world implementation and that there may be better ways to implement the algorithm. For this question, you need only show that the algorithm will converge in a finite number of steps.

State any assumptions required, if any, for the algorithm to converge.

The goal of this course is to provide you with a comprehensive understanding of Machine Learning Algorithms:

Prep for your machine learning interview with these resources from Interview Query: