Machine Learning Interview Questions

Review this list of 123 Machine Learning interview questions and answers verified by hiring managers and candidates.

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Is the Mean Squared Error (MSE) a suitable cost function for a logistic regression model?
1 answer
Tell me about a machine learning project you worked on and its outcome.
3 years ago
Tell me about a machine learning research topic you find interesting.
3 years ago
Which is more likely to result in overfitting a model? More training data, decreased nodes in neural network hidden layers, eliminating sparse features, or using a Gaussian or RBF kernel in SVM?
1 answer
How would you go about scoping improvements to the quality of a machine learning model?
9 months ago
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What is the significance of an area under the curve (AUC) equal to 0.5?
1 answer
Explain linear regression to a non-technical stakeholder.
Describe the model you would build to predict fraudulent credit card transactions.
Describe how random forests work and explain what an ensemble method is.
1 answer
What is XGBoost?
3 months ago
Design a product that can predict when someone is getting a paycheck.
6 months ago
How have you led teams at scale to improve models or algorithms post-training, especially using contractors?
a year ago
What is entropy and how is it used in machine learning?
What are the assumptions of linear regression, and what happens if they are violated?
What metrics would you consider to assess your model's performance?
What is the difference between batch and real-time predictions?
Tell me about a forecasting model you built that didn't match real-world results.
Explain linear regression to a technical stakeholder.
How are tree combinations from boosting methods different from those used in random forests?
Tell me about a time you deployed a machine-learning model into production. How did it fit into the production environment and what tools did you use?

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Machine Learning Interview Questions

Is the Mean Squared Error (MSE) a suitable cost function for a logistic regression model?

Tell me about a machine learning project you worked on and its outcome.

Tell me about a machine learning research topic you find interesting.

Which is more likely to result in overfitting a model? More training data, decreased nodes in neural network hidden layers, eliminating sparse features, or using a Gaussian or RBF kernel in SVM?

How would you go about scoping improvements to the quality of a machine learning model?

What is the significance of an area under the curve (AUC) equal to 0.5?

Explain linear regression to a non-technical stakeholder.

Describe the model you would build to predict fraudulent credit card transactions.

Describe how random forests work and explain what an ensemble method is.

What is XGBoost?

Design a product that can predict when someone is getting a paycheck.

How have you led teams at scale to improve models or algorithms post-training, especially using contractors?

What is entropy and how is it used in machine learning?

What are the assumptions of linear regression, and what happens if they are violated?

What metrics would you consider to assess your model's performance?

What is the difference between batch and real-time predictions?

Tell me about a forecasting model you built that didn't match real-world results.

Explain linear regression to a technical stakeholder.

How are tree combinations from boosting methods different from those used in random forests?

Tell me about a time you deployed a machine-learning model into production. How did it fit into the production environment and what tools did you use?

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