Interview Q&A / Machine Learning
Machine Learning interview questions
Supervised vs unsupervised learning, overfitting, metrics and the classic models, explained simply.
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- How do you handle missing values in a dataset?
First understand why values are missing. Then drop rows or columns if few are affected, or impute with mean, median, mode, a constant, or a model-based estimate. Adding a 'was missing' flag often helps. Fit the imputer on training data only.
BeginnerQuick answer1 min read - Why do we scale features, and which algorithms need it?
Scaling puts features on comparable ranges. Distance-based and gradient-based models (KNN, SVM, k-means, PCA, neural networks, regularised regression) need it. Tree-based models do not.
BeginnerQuick answer1 min read