What is the role of temperature in LLM APIs?
It adjusts the probability distribution of predicted tokens. 0 makes it deterministic, higher values increase creativity.
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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 oversampling minority classes (SMOTE), undersampling majority classes, or using class weights in the loss function.
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