Reference A starting point
No Free Lunch Theorem
The principle that no single ML algorithm works best for all problems - algorithm choice depends on the specific task.
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Foundations Inductive Bias Assumptions built into a learning algorithm that guide it toward certain solutions over others. Foundations Occam's Razor The principle that simpler models should be preferred when they perform equally well, reducing overfitting. Foundations Bias-Variance Tradeoff The balance between a model's bias (systematic error) and variance (sensitivity to training data fluctuations).