DATA MINING and MACHINE LEARNING. PREDICTIVE TECHNIQUES: ENSEMBLE METHODS, BOOSTING, BAGGING, RANDOM FOREST, DECISION TREES and REGRESSION TREES.: Examples with MATLAB

Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques) , which trains a model on known input and output data so that it can predict future outputs, and descriptive techniques (unsupervised techniques) , which finds hidden patterns or intrinsic structures in input data. The aim of predictive techniques is to build a model that makes predictions based on evidence in the presence of uncertainty. A predictive algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Predictive techniques uses regression techniques to develop predictive models. This book develoop ensemble methods, boosting, bagging, random forest, decision trees and regression trees. Exercises are solved with MATLAB software.