Data Cleaning and Exploration with Machine Learning

Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression

As is true for support vector machines, K-nearest neighbors and decision tree models are best known as classification models. However, they can also be used for regression and present some advantages over classical linear regression. K-nearest neighbors and decision trees can handle nonlinearity well and no assumptions regarding the Gaussian distribution of features need to be made. Moreover, by adjusting our value of k for K-nearest neighbors (KNN) or maximal depth for decision trees, we can avoid fitting the training data too precisely.

This brings us back to a theme from the previous two chapters – how to increase model complexity, including accounting ...