DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES

César Pérez López

eBook (epub)

(0)

Donner la première évaluation

Data Mining and Machine Learning uses two types of techniques: predictive techniques (supervised techniques), which trains a mode...

CHF12.00

Download est disponible immédiatement

eBook (epub)

Description

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.

Informations sur le produit

Titre:

DATA MINING and MACHINE LEARNING. CLASSIFICATION PREDICTIVE TECHNIQUES: SUPPORT VECTOR MACHINE, LOGISTIC REGRESSION, DISCRIMINANT ANALYSIS and DECISION TREES

Sous-titre :

Examples with MATLAB

Auteur:

César Pérez López

EAN:

9781471786921

Format:

eBook (epub)

Producteur:

Lulu.com

Genre:

Bases