101 Machine Learning Algorithms and Model Selection Questions

All 101 questions

How does bagging work, and how does it differ from boosting?
Asked in 6 of 320 interviews · last asked Oct 1 · Concept · Junior
Show answer
Bagging trains models independently on bootstrap samples: random samples drawn with replacement from the training data. It combines predictions by averaging or voting, mainly reducing variance. Boosting builds models in sequence, with each new model targeting errors in the current combined prediction. Boosting often reduces bias, but can be more sensitive to noise.
- Bagging models can train in parallel; boosting rounds usually depend on earlier rounds.
- Boosting may change example weights or fit loss gradients, depending on the method.
- Random forests use bagging plus random feature selection; AdaBoost and gradient boosting use boosting.
What the interviewer checks: The interviewer checks whether you understand how models are trained, combined, and used to reduce different errors.
Other ways it was asked
- “What is the difference between bagging and boosting?”
- “What is the basic difference between bagging and boosting?”
- “Could you describe the difference between boosting and bagging models?”
What is XGBoost?
Asked in 4 of 320 interviews · last asked Sep 19 · Concept · Junior
Show answer
XGBoost stands for Extreme Gradient Boosting. It is a machine learning library commonly used to build boosted decision trees. It adds trees one at a time, using loss gradients to reduce errors in the current model. Regularization, learning rate, and tree limits help control overfitting.
- Commonly used for classification and regression on tabular data.
- Uses first and second derivatives of the loss to guide tree building.
- Supports missing values and efficient training.
- Can still overfit, so tune model settings and use validation data for early stopping.
What the interviewer checks: The interviewer checks whether you understand how XGBoost builds trees sequentially and controls overfitting.
Other ways it was asked
- “What is XGBoost, and how does it work?”
- “How does XGBoost work?”
- “Explain XGBoost.”
How does a random forest work?
Asked in 3 of 320 interviews · last asked Oct 1 · Concept · Junior
Other ways it was asked
- “What is a random forest?”
What is logistic regression?
Asked in 3 of 320 interviews · last asked Aug 27 · Concept · Junior
Other ways it was asked
- “What is logistic regression, and how does it work?”
- “Can you explain logistic regression?”
What are some common supervised and unsupervised machine learning algorithms?
Asked in 3 of 320 interviews · last asked Aug 21 · Concept · Junior
Other ways it was asked
- “Can you name a few commonly used machine learning algorithms?”
- “What are examples of algorithms used for supervised and unsupervised learning?”
- “What are some common algorithms used in supervised and unsupervised learning, and how do they differ?”
How do you choose the right machine learning model for a problem and assess its fit?
Asked in 2 of 320 interviews · last asked Aug 22 · Concept · Mid-level
Other ways it was asked
- “How do you choose the right ML algorithm for a problem?”
- “How do you choose the right machine-learning algorithm for a problem?”
- “Which models would you choose for an AI application, and how would you evaluate their fit?”
What type of algorithm is K-Nearest Neighbors (KNN)?
Asked in 2 of 320 interviews · last asked Aug 13 · Concept · Junior
Other ways it was asked
- “How does the K-nearest neighbors (KNN) algorithm work, and when is it useful?”
When choosing between models, how do you assess whether the simpler model's interpretability is worth potentially sacrificing some accuracy?
Asked in 2 of 320 interviews · last asked Aug 31 · Scenario · Mid-level
Other ways it was asked
- “If you had to choose between deploying a highly accurate but complex model and a slightly less accurate but more interpretable one, how would you make that decision?”
- “How would you choose between two predictive models, one with higher accuracy but lower interpretability and the other the opposite?”
What roles do support and confidence play in association rule mining?
Asked once · last asked May 2 · Concept · Mid-level
Other ways it was asked
- “How are support and confidence used in association rule mining?”
- “What is all-confidence in association rule mining?”
What types of constraints are used in association rule mining?
Asked once · last asked May 2 · Concept · Mid-level
How are principal components ranked in PCA?
Asked once · last asked May 25 · Concept · Mid-level
Other ways it was asked
- “Why are principal components ranked that way in PCA?”
How do you decide whether to use traditional machine learning or deep learning for a problem?
Asked once · last asked Sep 22 · Concept · Senior
How do support vector machines (SVMs) classify data?
Asked once · last asked Apr 20 · Concept · Junior
In what scenarios would you use an SVM model?
Asked once · last asked Apr 20 · Concept · Junior
For predicting whether a student will score above 90%, would an SVM or a decision tree be more appropriate, and how would you compare the two?
Asked once · last asked Apr 20 · Scenario · Mid-level
What role does a hyperplane play in an SVM?
Asked once · last asked Apr 20 · Concept · Junior
How would you make a financial risk model that produces scores or percentages more interpretable, and how would you involve human reviewers so banks can understand its recommendations?
Asked once · last asked Apr 20 · Scenario · Senior
Which measures, such as lift and chi-square, can be used to assess associations in rule mining?
Asked once · last asked May 2 · Concept · Mid-level
Why would you use a classification model, and what kinds of problems is it suited for?
Asked once · last asked May 13 · Concept · Junior
How do you investigate unexpected model coefficients, such as promotion-related spikes, and determine whether they reflect real effects?
Asked once · last asked May 22 · Scenario · Senior
In your own words, what happens to the data and its coordinates in multidimensional space during PCA?
Asked once · last asked May 25 · Concept · Mid-level
What happens to the data before variance is calculated in PCA?
Asked once · last asked May 25 · Concept · Mid-level
How can you map data from N dimensions, such as five, to two dimensions for visualization, and how is that mapping obtained?
Asked once · last asked May 25 · Concept · Mid-level
How is the covariance matrix constructed in PCA?
Asked once · last asked May 25 · Concept · Mid-level
Does PCA preserve the interpretability of data?
Asked once · last asked May 25 · Concept · Mid-level
How can clustering be used for dimensionality reduction?
Asked once · last asked May 25 · Concept · Mid-level
After clustering data, how can you reduce its dimensionality while preserving the cluster labels?
Asked once · last asked May 25 · Scenario · Mid-level
How can similarities between data points be used to reduce a dataset's dimensionality?
Asked once · last asked May 25 · Concept · Mid-level
When would you choose logistic regression over a tree-based model, and what assumptions or tradeoffs would drive that choice?
Asked once · last asked Jun 3 · Concept · Mid-level
For log-transformed numeric predictors in logistic regression, how would you check whether the logit is approximately linear in those features, and what would you do if it isn't?
Asked once · last asked Jun 3 · Scenario · Mid-level
Other ways it was asked
- “For mostly straightforward logged numeric predictors, how would you check whether the logit is approximately linear in those features, and what would you do if it isn't?”
After visual checks, what diagnostic or statistical test would you use to assess logit linearity more formally?
Asked once · last asked Jun 3 · Concept · Mid-level
How do you map predicted coordinate points to application requirements, and how do you choose a prediction model?
Asked once · last asked Jul 16 · Scenario · Mid-level
How would you choose a model and design the architecture for an AI solution?
Asked once · last asked Jul 16 · Design · Senior
Other ways it was asked
- “Describe an AI solution you built: what model did you choose, why did you choose it, and what was the architecture?”
Why does bagging primarily reduce variance rather than bias?
Asked once · last asked Jul 16 · Concept · Senior
Other ways it was asked
- “Why does bagging reduce variance specifically, not bias?”
When would you use logistic regression or boosting for tabular data?
Asked once · last asked Jul 20 · Concept · Mid-level
How do you compare candidate models, choose an evaluation metric, and assess whether feature engineering improves validation performance?
Asked once · last asked Jul 21 · Concept · Mid-level
If interpretability is not a priority when developing AI systems, what should be prioritized instead?
Asked once · last asked Jul 25 · Concept · Senior
Show Python code for logistic regression and how you would use it to get a yes/no prediction.
Asked once · last asked Jul 26 · Write code · Junior
Can you write Python code for a linear regression algorithm, from importing the data through making predictions?
Asked once · last asked Aug 11 · Write code · Mid-level
What are boosting and ensemble methods, and how do they work?
Asked once · last asked Aug 11 · Concept · Mid-level
What is the difference between a random forest and a decision tree?
Asked once · last asked Aug 11 · Concept · Junior
What are the differences between clustering, classification, and regression?
Asked once · last asked Aug 13 · Concept · Junior
If experimental measurements have heteroscedastic noise and occasional outliers, how would you fit a model and diagnose whether residual structure reflects physics mis-specification versus measurement artifacts?
Asked once · last asked Aug 17 · Scenario · Senior
How do you ensure your models remain interpretable, especially when dealing with complex data?
Asked once · last asked Aug 22 · Scenario · Mid-level
Can you explain the difference between logistic regression, random forest, and SVM?
Asked once · last asked Sep 2 · Concept · Mid-level
How do Bayesian models compare with logistic regression, random forest, and SVM, and why might a Bayesian model be the best choice?
Asked once · last asked Sep 2 · Concept · Mid-level
What are some common methods or algorithms used in data mining?
Asked once · last asked Sep 9 · Concept · Mid-level
How would you build a linear regression model to predict monthly spend from user behavior and account characteristics?
Asked once · last asked Sep 18 · Write code · Mid-level
How do you segment users into different buckets rather than building bespoke features for each individual user?
Asked once · last asked Sep 18 · Case · Senior
How would you choose and implement an approach for a given use case: traditional ML, deep learning (such as an autoencoder, neural network, or CNN), an agentic system, RAG, or fine-tuning?
Asked once · last asked Sep 22 · Design · Mid-level
Other ways it was asked
- “How would you determine, based on a problem's use case, whether to use a traditional ML solution, a deep learning model (such as an autoencoder, neural network, or CNN), an agentic system, RAG, or fine-tuning, and how would you implement that choice?”
How do you approach model interpretability and explainability using SHAP and baseline data analysis?
Asked once · last asked Sep 22 · Concept · Senior
How would you provide users with instance-level explanations for a model's prediction?
Asked once · last asked Sep 22 · Behavioral · Senior
Other ways it was asked
- “Tell me about a time you provided users with instance-level explanations for a model's prediction.”
Would predicting coal gross calorific value be a classification or regression problem, and why?
Asked once · last asked Sep 22 · Concept · Mid-level
What criteria would you use to choose the best model for a predictive modeling problem?
Asked once · last asked Sep 22 · Concept · Senior
What statistical or machine learning model would you use to estimate whether a user will click on a template?
Asked once · last asked Sep 22 · Concept · Mid-level
How would you explain which features are most important if you use logistic regression?
Asked once · last asked Sep 22 · Concept · Mid-level
What is the difference between random forest and linear regression?
Asked once · last asked Sep 23 · Concept · Junior
How do you select models for different AI workloads?
Asked once · last asked Sep 24 · Scenario · Senior
Can you share an example of a model or machine learning system that underperformed, and how you diagnosed the issue?
Asked once · last asked Sep 28 · Behavioral · Mid-level
In an XGBoost model, what is the individual model that gets boosted?
Asked once · last asked Sep 29 · Concept · Mid-level
How do SHAP values work for an XGBoost model, and what do they represent?
Asked once · last asked Sep 29 · Concept · Mid-level
Other ways it was asked
- “What do SHAP values mean for a model’s predictions?”
What does a SHAP value mean, and what does a high SHAP value for age indicate?
Asked once · last asked Sep 29 · Concept · Mid-level
How do you select a model, assess its effectiveness, and choose evaluation metrics?
Asked once · last asked Oct 9 · Concept · Mid-level
What is the difference between parametric and nonparametric models, and why are they called parametric and nonparametric?
Asked once · last asked Oct 12 · Concept · Mid-level
How would you compare the strengths and weaknesses of at least two locally run AI models, and when would you choose one over the other?
Asked once · last asked Oct 15 · Concept · Senior
Other ways it was asked
- “How do you compare the strengths and weaknesses of at least two local AI models you have used, and when do you choose one over the other?”
How do you choose the right architecture for a problem when multiple models could work?
Asked once · last asked Oct 17 · Concept · Mid-level
How would you select classification models, compare their performance, and approach feature selection and engineering?
Asked once · last asked Oct 19 · Scenario · Mid-level
Other ways it was asked
- “What specific classification models did you consider? And what criteria did you use to compare their performance? Can you describe the process you followed for feature selection and engineering?”
How would you choose a classification model, such as logistic regression or a decision tree, over alternatives and handle class imbalance in the dataset?
Asked once · last asked Oct 19 · Scenario · Mid-level
Other ways it was asked
- “Can you give an example of a specific model you used such as logistic regression or decision tree? And explain why you chose it over others. How did you handle class imbalance if it was present in your dataset?”
How do you approach choosing the right analytical model or library for a new task?
Asked once · last asked Oct 19 · Scenario · Mid-level
Why would you use XGBoost, and not logistic regression or a neural network?
Asked once · last asked Oct 20 · Concept · Mid-level
How would you choose, implement, and refine an ensemble or fusion strategy?
Asked once · last asked Oct 20 · Behavioral · Senior
Other ways it was asked
- “How did you choose and implement an ensemble or fusion strategy, and what changes did you make yourself?”
Which machine-learning models would you consider for detecting fraudulent banking transactions?
Asked once · last asked Oct 22 · Concept · Mid-level
What are regression models, and when would you use them?
Asked once · last asked Jan 13 · Concept · Junior
How do you interpret SHAP values for correlated features, and what steps would you take to avoid misleading conclusions from SHAP-based explanations?
Asked once · last asked Oct 23 · Scenario · Senior
What dimensionality reduction methods do you know?
Asked once · last asked May 25 · Concept · Junior
Other ways it was asked
- “What other dimensionality reduction methods do you know?”
How do you see the role of emerging techniques like AutoML impacting traditional predictive modeling processes?
Asked once · last asked Aug 29 · Concept · Mid-level
Other ways it was asked
- “How do you think emerging techniques like AutoML affect the role of a data scientist?”
What is the difference between classification and regression?
Asked once · last asked Mar 4 · Concept · Junior
What kind of problems can we solve using regression?
Asked once · last asked Mar 4 · Concept · Junior
What is a best-fit line, and how do we draw it?
Asked once · last asked Mar 4 · Concept · Junior
What is clustering?
Asked once · last asked Mar 25 · Concept · Junior
What is the K-means algorithm?
Asked once · last asked Mar 25 · Concept · Junior
What is hierarchical clustering?
Asked once · last asked Mar 25 · Concept · Junior
What is linear regression?
Asked once · last asked Mar 25 · Concept · Junior
How do you map data from N dimensions to two dimensions using PCA?
Asked once · last asked May 25 · Concept · Mid-level
What is a support vector machine (SVM): a machine-learning algorithm or an application?
Asked once · last asked Jul 13 · Concept · Junior
Is logistic regression a Python library or a machine learning algorithm?
Asked once · last asked Jul 26 · Concept · Junior
What is ensemble learning?
Asked once · last asked Jul 26 · Concept · Junior
What is SHAP?
Asked once · last asked Jul 26 · Concept · Junior
Explain regression.
Asked once · last asked Aug 1 · Concept · Junior
Why do we use regression, classification, and clustering methods?
Asked once · last asked Aug 11 · Concept · Junior
Can you name two algorithms used for classification and two used for clustering?
Asked once · last asked Aug 13 · Concept · Junior
What does K-Means do, and is it a classification or clustering algorithm?
Asked once · last asked Aug 13 · Concept · Junior
Why is memory important when selecting the right model?
Asked once · last asked Aug 20 · Concept · Mid-level
Can you explain support vector machines?
Asked once · last asked Aug 27 · Concept · Junior
Other ways it was asked
- “Can you explain support vector machine?”
What algorithm would you use for anomaly detection?
Asked once · last asked Sep 10 · Concept · Mid-level
Why would you use random forest?
Asked once · last asked Sep 10 · Concept · Junior
When would you choose segmentation instead of dimensionality reduction?
Asked once · last asked Sep 11 · Concept · Mid-level
What is the difference between clustering and classification?
Asked once · last asked Sep 16 · Concept · Junior
Should a multiclass classification model return a probability for each category?
Asked once · last asked Sep 22 · Concept · Mid-level
What do you see as the future of explainable AI?
Asked once · last asked Oct 5 · Concept · Senior
Which unsupervised learning algorithm do you use most, and what problem does it address?
Asked once · last asked Oct 17 · Concept · Mid-level

How this list is made

Every question here was asked by a real interviewer in a real job interview that a candidate ran MeetAssist in. After the call, the interviewer's questions are pulled from the transcript. Practice sessions, tests, exams and anything that was not a job interview are dropped.

Questions are rewritten so they make sense on their own: names of people, employers and clients are removed, and speech-to-text mistakes are fixed. Small talk, logistics and questions about one candidate's own résumé are left out. Rephrasings of the same question are merged, and the count is the number of different interviews it came up in.

The interviews come from candidates who use MeetAssist, so they lean towards software, data and AI roles: the shares describe these interviews, not every Machine Learning interview. Answers to the most asked questions are written and checked by an editor; a "common mistake" is shown only when reviews of real answers in these interviews show it, with the count.

320 interviews, 221 candidates, Nov 2025 to Oct 2026. Updated every month with new interviews.

All 101 questions

How does bagging work, and how does it differ from boosting?

What is XGBoost?

How does a random forest work?

What is logistic regression?

What are some common supervised and unsupervised machine learning algorithms?

How do you choose the right machine learning model for a problem and assess its fit?

What type of algorithm is K-Nearest Neighbors (KNN)?

When choosing between models, how do you assess whether the simpler model's interpretability is worth potentially sacrificing some accuracy?

What roles do support and confidence play in association rule mining?

What types of constraints are used in association rule mining?

How are principal components ranked in PCA?

How do you decide whether to use traditional machine learning or deep learning for a problem?

How do support vector machines (SVMs) classify data?

In what scenarios would you use an SVM model?

For predicting whether a student will score above 90%, would an SVM or a decision tree be more appropriate, and how would you compare the two?

What role does a hyperplane play in an SVM?

How would you make a financial risk model that produces scores or percentages more interpretable, and how would you involve human reviewers so banks can understand its recommendations?

Which measures, such as lift and chi-square, can be used to assess associations in rule mining?

Why would you use a classification model, and what kinds of problems is it suited for?

How do you investigate unexpected model coefficients, such as promotion-related spikes, and determine whether they reflect real effects?

In your own words, what happens to the data and its coordinates in multidimensional space during PCA?

What happens to the data before variance is calculated in PCA?

How can you map data from N dimensions, such as five, to two dimensions for visualization, and how is that mapping obtained?

How is the covariance matrix constructed in PCA?

Does PCA preserve the interpretability of data?

How can clustering be used for dimensionality reduction?

After clustering data, how can you reduce its dimensionality while preserving the cluster labels?

How can similarities between data points be used to reduce a dataset's dimensionality?

When would you choose logistic regression over a tree-based model, and what assumptions or tradeoffs would drive that choice?

For log-transformed numeric predictors in logistic regression, how would you check whether the logit is approximately linear in those features, and what would you do if it isn't?

After visual checks, what diagnostic or statistical test would you use to assess logit linearity more formally?

How do you map predicted coordinate points to application requirements, and how do you choose a prediction model?

How would you choose a model and design the architecture for an AI solution?

Why does bagging primarily reduce variance rather than bias?

When would you use logistic regression or boosting for tabular data?

How do you compare candidate models, choose an evaluation metric, and assess whether feature engineering improves validation performance?

If interpretability is not a priority when developing AI systems, what should be prioritized instead?

Show Python code for logistic regression and how you would use it to get a yes/no prediction.

Can you write Python code for a linear regression algorithm, from importing the data through making predictions?

What are boosting and ensemble methods, and how do they work?

What is the difference between a random forest and a decision tree?

What are the differences between clustering, classification, and regression?

If experimental measurements have heteroscedastic noise and occasional outliers, how would you fit a model and diagnose whether residual structure reflects physics mis-specification versus measurement artifacts?

How do you ensure your models remain interpretable, especially when dealing with complex data?

Can you explain the difference between logistic regression, random forest, and SVM?

How do Bayesian models compare with logistic regression, random forest, and SVM, and why might a Bayesian model be the best choice?

What are some common methods or algorithms used in data mining?

How would you build a linear regression model to predict monthly spend from user behavior and account characteristics?

How do you segment users into different buckets rather than building bespoke features for each individual user?

How would you choose and implement an approach for a given use case: traditional ML, deep learning (such as an autoencoder, neural network, or CNN), an agentic system, RAG, or fine-tuning?

How do you approach model interpretability and explainability using SHAP and baseline data analysis?

How would you provide users with instance-level explanations for a model's prediction?

Would predicting coal gross calorific value be a classification or regression problem, and why?

What criteria would you use to choose the best model for a predictive modeling problem?

What statistical or machine learning model would you use to estimate whether a user will click on a template?

How would you explain which features are most important if you use logistic regression?

What is the difference between random forest and linear regression?

How do you select models for different AI workloads?

Can you share an example of a model or machine learning system that underperformed, and how you diagnosed the issue?

In an XGBoost model, what is the individual model that gets boosted?

How do SHAP values work for an XGBoost model, and what do they represent?

What does a SHAP value mean, and what does a high SHAP value for age indicate?

How do you select a model, assess its effectiveness, and choose evaluation metrics?

What is the difference between parametric and nonparametric models, and why are they called parametric and nonparametric?

How would you compare the strengths and weaknesses of at least two locally run AI models, and when would you choose one over the other?

How do you choose the right architecture for a problem when multiple models could work?

How would you select classification models, compare their performance, and approach feature selection and engineering?

How would you choose a classification model, such as logistic regression or a decision tree, over alternatives and handle class imbalance in the dataset?

How do you approach choosing the right analytical model or library for a new task?

Why would you use XGBoost, and not logistic regression or a neural network?

How would you choose, implement, and refine an ensemble or fusion strategy?

Which machine-learning models would you consider for detecting fraudulent banking transactions?

What are regression models, and when would you use them?

How do you interpret SHAP values for correlated features, and what steps would you take to avoid misleading conclusions from SHAP-based explanations?

What dimensionality reduction methods do you know?

How do you see the role of emerging techniques like AutoML impacting traditional predictive modeling processes?

What is the difference between classification and regression?

What kind of problems can we solve using regression?

What is a best-fit line, and how do we draw it?