Question Bank
Machine Learning interview questions
Naming a model or a metric is only the start of an interview answer. Explain why it fits the task, and connect training, regularization, validation and evaluation to a concrete example. Start with your own explanation, then state its assumptions and limitations.
Showing 20 of 746 questions
What is cross-validation and why is it important in machine learning?
Explain the difference between bias and variance and how this trade-off affects a model.
When should Dropout be used in neural networks and how does it affect training?
What is early stopping and how does it help in model training?
How does the Bagging ensemble method work and what are its main advantages?
Explain what PCA is and how it can be used for dimensionality reduction.
What does model.eval() do in PyTorch and why is it needed?
What is the difference between Batch Normalization and Layer Normalization?
How to build metrics for demand forecasting model evaluation? What features are important for demand forecasting?
How to handle missing values in sales data?
How to properly impute data considering seasonality? Is it correct to fill December missing values with yearly average?
Why do banking recommendation systems use interpretable models (trees, LGBM) instead of deep learning?
Tell me in detail about one ML task: approach, beautiful idea under the hood.
How to encode high-cardinality categorical feature (100 ports) for models that do not support categories?
What error will we make if we calculate target encoding on the entire train set for time series?
Tell me about main binary classification metrics: precision, recall, accuracy, F1, ROC-AUC.
Why does F1-score use harmonic mean instead of arithmetic mean? What is the advantage?
A random classifier outputs 1 with probability 60%. Sample: 30 ones, 70 zeros. What are precision and recall?
Which model extrapolates better beyond the training set: decision tree, random forest, gradient boosting, KNN, linear regression, neural network?
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