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.
Classification models predict categorical responses, for example,
whether an email is genuine or spam, or whether a tumor is cancerous or
benign. Typical applications include medical research, fraud detection,
and credit scoring. This book develops the most important classification
predictive techniques: Logistic regression, discriminant analysis,
decision trees and classification support vector machine. Exercises are
solved with MATLAB software.