Exclusive 10 Machine Learning Interview Questions and Answers for 2022

Interview Questions

When interviewing for a position as a data scientist, data engineer, or machine learning engineer, the interviewer may ask you questions about artificial intelligence and machine learning. Your answers to these specific interview questions can allow employers to analyze your experience and match you with a machine learning role. Understanding the different interview questions you may encounter can help you demonstrate your machine learning expertise during the interview.

In this article, we discuss 10 common machine learning interview questions and present tips to help you prepare for the interview.

10 Machine Learning Questions with Model Answers

Learning how to answer the following machine learning interview questions can help you ace the interview:

1. Can you explain the difference between bias and variance?

When training a machine learning model, increasing its bias can often reduce variance. High variance can reduce distortion. The interviewer may ask you to test your understanding of when to optimize for bias or variance in different situations. You can respond by defining bias and variance and explaining how they affect model performance.

Example: “Bias and variance are metrics that engineers practice when working out models. Bias measures how well the model’s predictions match the exact descriptions of the training data. Variance is how much the model’s predictions change when we test it on a different data set. A model with high bias simplifies assumptions about the data and performs poorly. When variance is high, the model overfits the training data and can be sensitive to noise.”

2. What algorithm would you custom in the situation of low bias and high variance?

Depending on the goal of training a machine learning model, you may prefer to reduce bias and ignore variance. Learning algorithms are unable to reduce these two metrics simultaneously. The interviewer may ask this to evaluate how you can reduce bias when it is more important than variance. You can answer by showing how a data scientist can identify and change bias or variance in a model.

Example: “A low-bias model shows marginal fault during exercise. If the variance is extraordinary, the model has a low exercise fault but a large fault on the test set. You can reduce the complexity of the model or increase the training data to reduce the variance. For example, if you use a bagging algorithm as a random forest and you notice a lot of variance, you can use one model instead. A simpler model reduces complexity and can limit the model’s overfitting behavior.”

3. Explain the relationship between repetition and true positive rate.

The true positive rate can help machine learning experts identify the percentage of positive samples that the model correctly classifies as positive. A hiring manager can use this question to analyze your understanding of how true positivity affects recall. You can answer by defining the two terms and showing how they are related.

Example: “True positive (TP) rate is the possibility that the model recognizes a positive sample as positive. You can express it using the formula TP / (TP + FN). The false negative (FN) is the number of positive points of the model, falsely marked as negative. Download is equivalent to the true positive rate. The withdrawal formula is the same as the true positive rate formula.”

4. When can you prefer comb regression over lasso regression?

Regression techniques can reduce the variance of a model by penalizing its weights. Depending on the goal of the project, ridge regression may be a better controller than lasso. The interviewer may ask you to explore how you decide between these two regularizers. You can answer by explaining how the two techniques work and when each is more beneficial.

Example: “The option may be subject to the objective of the regularization. Lasso regression produces sparse features, assigning zeros to unimportant features of the model. Ridge assigns a regularizer weight to each feature: significant features have higher weights, while minor features have smaller weights. Unlike lasso, ridge avoids setting unimportant features to zero. You can prefer lasso when selecting features because it filters out the unimportant ones. Ridge is better when features are correlated and you want to keep them all.”

5. Explain how you select variables when working with a data set.

The data you train the model with can affect its performance. The hiring manager may request an evaluation of your ability to filter out low-quality data that may negatively impact model performance. To answer, you can explain the process you used to select the variables in the data set.

Example: “When working on a data set, you can select variables by examining the data. Analyzing the data can help you decide which variables might be most useful in revealing relationships between different data points. You can also look at the variable names and descriptions to better understand the representation of each variable. For example, if you are using a dataset with information about different types of animals, one of the goals might be to distinguish between animals. You can select variables such as ‘species’ and ‘weight’ to display the animal with clear differences.”

6. Describe the variances among correlation and covariance.

Correlation and covariance are metrics that can help a machine learning engineer understand how different variables are related. The interviewer may test your ability to identify relationships in a data set, which may help you select training variables. You can answer by defining the two terms and showing how they differ.

Example: “Covariance displays how the mean of one variable X varies from the mean of another variable Y. For example, if an increase in X causes a corresponding increase in Y, the two variables have a positive covariance. Covariance measures the direction of the linear relationship between X and Y. Correlation measures the strength of the relationship between two variables.”

7. What is the difference between random forest and gradient boosting algorithms?

Random forests and gradient boosting algorithms are learning skills you can practice for sorting and reversion complications. This question measures your ability to apply tree algorithms to machine learning problems. You can answer by explaining the basic differences between the two methods.

Example: “Random forest algorithms use bagging algorithms. Bagging combines different independent models and averages their predictions. Merging models can help reduce variance. You can use gradient boosting to turn weak learners into stronger ones. A weak learner can be a feature that performs slightly better than random. You can make stronger learners from a weak model by taking its predictions and putting more weight on the misclassified samples. This weighting creates a new data set that you can then use to train a better model.”

8. Explain the meaning of convex hull.

You can describe a convex hull as a way of placing a polygon (a two-dimensional shape) on a set of data points. The interviewer may ask this to test your mathematical, critical thinking and analytical skills when working with data systems. You can answer by describing the convex hull and how it can be useful in machine learning.

Example: “A convex hull marks the outer boundaries of two groups of data points in a data set that an algorithm can linearly separate. The result of creating a convex hull is the hyperplane of the maximum edge. This edge can extend the line of separation between the two data groups.”

9. Would you use K-fold or LOOCV cross-validation on a time series data set?

Validation is a technique you can use to evaluate the performance of your model on unseen data. A hiring manager may ask this question to test your ability to determine when it is appropriate to use a particular validation technique on a data set. Can you explain the two validation techniques and where they are appropriate?

Example: “K-fold cross-validation divides the original data into K subsets. Engineers can use K-1 subsets to train the model and the remaining subset to test the model. The algorithm repeats the process K times. The average error over all K trials is an estimate of the model’s error.

In leave-one-out cross-validation, you use all but one data point to train the model. You then test the model on the remaining data point. If there is significant correlation in the time series data, LOOCV may be more appropriate. The suitability is because LOOCV can provide more accurate results than K-fold.”

10. Why wouldn’t you use the Manhattan distance to calculate the nearest neighbor distance using K-means or KNN?

In distance-based machine learning techniques, the Euclidean distance may be preferable to the Manhattan distance. This question can help the interviewers test your experience using different distance calculations and distance algorithms like KNN. You can answer by showing the advantage of Euclidean distance compared to Manhattan distance.

Example: “Euclidean distance is the shortest path between a source and a destination point. Using the Manhattan distance, you get the sum of all distances between a source and a destination point. K-means and KNN trust on the outcome the smallest space among the two destinations. The Manhattan distance between two points may not be shortest. This effect may make Manhattan a poor metric for such algorithms.”

Interview Tips with Machine Learning

Here are some tips that can help you prepare for a machine learning interview:

  1. Continue coding. The interviewer may test your ability to write efficient code. With practice, you may better remember how to approach different programming problems during the interview.
  2. Learn about machine learning frameworks. Different companies may use different tools to build their machine learning algorithms. You can research the company and learn the basics of the tools they use.
  3. Take a machine learning course. Courses can help you remember basic machine learning techniques. Understanding basic topics such as error types will allow you to tackle more complex machine learning problems.

Other Questions you can expect in a Machine Learning Interview

As you progress through the interview, the hiring manager can ask more detailed machine learning interview questions to assess your technical, communication, and problem-solving skills. Studying the following common machine learning questions can help you prepare to answer them.

  • Clarify the variance among supervised and unsupervised machine learning.
  • Explain PCA and how it works.
  • Outline how the ROC curve works.
  • Distinguish between KNN and K-means clustering.
  • Explain accuracy and remember.
  • Show how Bayes’ theorem works.
  • Distinguish between L1 and L2 regularization.
  • Tell me about a preferred algorithm that you normally use.
  • Explain the differences between Type I and Type II error.
  • Describe what happens if you do not rotate the components in the PCA.
  • Explain the method you use to evaluate a logistic regression model.
  • Tell me how you choose which algorithm to practice when assuming a dataset.
  • Is regularization necessary in machine learning?
  • Give three examples of data preprocessing methods you would use to manage outliers.
  • Explain how you would reduce dimensionality.
  • Explain the advantages and disadvantages of using decision trees.
  • List the advantages and disadvantages of neural networks.
  • Describe whether file models are better than individual models.
  • Explain bagging.
  • Define confusion matrix.

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