Inductive Bias
Before learning a model given a data and a learning
algorithm, there are a few assumptions a learner makes about the algorithm.
These assumptions are called the inductive bias. It is like the property of the
algorithm.
For eg. in the case of decision trees, the depth of the tress is the
inductive bias. If the depth of the tree is too low, then there is too much
generalisation in the model. Similarly, if the depth of the tree is too much,
there is too less generalisation and while testing the model on a new example,
we might reach a particular example used to train the model. This may give us
incorrect results.
0 Comments