Inductive Bias is one of the major concepts in terms of machine learning. A data scientist spends much of the time to remove inductive bias (one of the major causes of overfitting).
A machine-learning algorithm with any ability to generalize beyond the training data that it sees has some type of inductive bias, which are the assumptions made by the model to learn the target function and to generalize training data.
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For example
In linear regression, the model implies that the output or dependent variable is related to the independent variable linearly (in the weights). This is an inductive bias of the model.
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