• Post category:StudyBullet-5
  • Reading time:5 mins read




What you will learn

 

Learn the fundamentals of decision trees in machine learning

 

Using the SPSS Modeler

 

Building a CHAID model

 

Using a lift and gains chart

 

Exploring algorithms

 

Building a tree interactively

Description

A tree has many analogies in real life, and turns out that it has influenced a wide area of machine learning, covering both classification and regression. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making.

If you’re working towards an understanding of machine learning, it’s important to know how to work with decision trees. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. 


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In this course, we’ll explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. 

We’ll also explore advanced concepts and details of decision tree algorithms.

This course is designed to give you a solid foundation on which to build more advanced data science skills.

 

English
language

Content

Introduction

Welcome
Introduction
Getting started

Decision Trees in IBM SPSS Modeler

Decision tree options in SPSS Modeler
Building CHAID model and add a second model with C&RT
Analysis nodes
Lift and gains chart

CHAID

What’s an algorithm
Chi-squared
Buliding a tree interactively
Bonferonni adjustment and level of measurement
CHAID

C&RT

Gini coefficient
Understanding C&RT
The complete C&RT tree
Stopping rules in CHAID and C&RT
Improving your model

QUEST

Understanding QUEST
How QUEST handles variables
How QUEST handles missing data
Pruning and stopping rules in QUEST

C5.0

ID3 and C4.5
Winnowing attributes and rule sets
Understanding information gain
Pruning in C5.0
How C5.0 handles missing data

Advanced Topics

Ensembles
Bagging
Random forests
Boosting
Costs and priors