Institution Instructor Course Date Machine Learning 1. Inductive bias refers to a host of varied assumptions made by a machine in predicting the results of a data set that the machine has not encountered before. Inductive bias in machine learning is used to derive impacts the bias-variance tradeoff in that while the user would have the minimal variance in order to get the best hypothesis from the algorithm this would mean accommodating bias. So the lower the variance learned the higher the bias accommodated and vice versa. [...]
Computer Science - Machine Learning Discuss the role of inductive bias in learning. In particular, see if you can come up with strategies for determining what bias(es) are associated with a given method. Discuss strengths and limitations that might arise from learning methods having inductive bias(es). To what extent might inductive bias exist beyond the traditional representation and selection biases? How do inductive biases impact the bias-variance tradeoff?