Inductive Bias
Inductive bias refers to the set of assumptions a learning algorithm makes to generalize from observed training data to unseen situations. These assumptions guide the model in selecting one hypothesis (a specific model configuration) over others that are equally consistent with the training data. Without inductive bias, a model would have no way to predict beyond the exact data it has already seen.
It's like a chef's personal cooking style or preferred ingredients, which influences how they interpret a recipe and prepare a dish, even if the recipe is vague.
Understanding and selecting appropriate inductive biases is crucial for building models that generalize well and avoid simply memorizing training data.
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