inductive bias in machine learning refers to the set of assumptions that a learning algorithm uses to make predictions on unseen data. It is the bias or set of preconceptions that a model has before learning from the data. It is necessary for a model to generalize from the training data to unseen data, as it helps the model to make decisions when the input data does not provide a clear or definite answer. Without inductive bias, a model would not be able to make predictions on new, unseen data.
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ah, so link not tracked to help with link not tracked to link not tracked information, sounds like