How do you handle class imbalance in datasets?
By oversampling minority classes (SMOTE), undersampling majority classes, or using class weights in the loss function.
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More FAQs in Top 30 AI and Machine Learning Interview Questions
A model with high bias underfits data, while a model with high variance overfits. You must find the balance that minimizes total error.
L1 (Lasso) shrinks weights to zero creating sparse models. L2 (Ridge) shrinks weights close to zero preventing dominant features.
By providing factual reference texts directly in the prompt, telling the model to limit its answers to the provided context.
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