Nearest Neighbors & Kernel Methods

Evaluate both statements.

Statement 1. The kernel density estimator is equivalent to performing (Nadaraya–Watson) kernel regression with the value $Y_i = 1/n$ at each point $X_i$ in the original data set.

Statement 2. The depth of a learned decision tree can be larger than the number of training examples used to create the tree.