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.