Qna Discussion

When the training set is small, a model that has a high bias and low variance seems to work better because they are less likely to overfit.
an algorithm with high bias is Linear Regression, Linear Discriminant Analysis and Logistic Regression.

For example, Naive Bayes works best when the training set is large. Models with low bias and high variance tend to perform better as they work fine with complex relationships.
Machine learning algorithms with low bias are Decision Trees, k-Nearest Neighbours and Support Vector Machines.