Gang Fang's Blog

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Inductive bias refers to the set of assumptions that a learner (human or machine) makes to predict outputs for previously unseen inputs when trying to learn from a given set of training examples. In other words, it’s the bias or set of preferences a learning algorithm has when inferring a hypothesis or function from observed data.

In the context of machine learning, algorithms cannot make predictions on previously unseen data without some form of inductive bias because there are an infinite number of hypotheses that might be consistent with any finite training set.

There are different types of inductive biases:

1. Preference for simpler hypotheses: One common inductive bias is the preference for simpler hypotheses over more complex ones. This is often referred to as Occam’s razor.
2. Restrictions on hypothesis space: For instance, in linear regression, the inductive bias is that the relationship between the inputs and the output is linear.
3. Prior knowledge: Some learning algorithms might have prior knowledge about the problem domain which they use to make better predictions. For instance, in a Bayesian learning algorithm, prior distributions over hypotheses represent this kind of bias.

The choice of inductive bias will influence how well a learning algorithm will perform on new, unseen data. A good inductive bias is one that leads to correct predictions on new data, while a poor one can lead to overfitting (where the model performs very well on the training data but poorly on new, unseen data) or underfitting (where the model is too simplistic to capture the underlying patterns in the data).

Understanding and choosing the appropriate inductive bias is crucial in the design of machine learning algorithms.

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