Machine Learning Interview Questions
What's the difference between supervised and unsupervised learning?
Supervised has labeled data; unsupervised doesn't. Example: regression vs. clustering."
Can you explain overfitting?
Overfitting is when a model fits training data too closely, performing poorly on newdata. Regularization techniques prevent it.
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Define bias-variance tradeoff.
Balancing bias (underfitting) and variance (overfitting) for optimal model performance. Techniques like cross-validation help."
What's feature engineering?
Transforming raw data into meaningful features for model training. Examples: scaling, one-hot encoding, and feature selection.
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How do you evaluate a classification model?
Metrics like accuracy, precision, recall, and F1 score assess its performance on test data. Choose based on project goals.
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Practice, stay confident, and nail that Machine Learning interview!