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Fundamentals of Decision Trees in Machine Learning

Learn the fundamentals of decision trees in maching learning
Instructor:
Tech Lab
3,348 students enrolled
English [Auto-generated]
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

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. 

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.

Introduction

1
Welcome
2
Introduction
3
Getting started

Decision Trees in IBM SPSS Modeler

1
Decision tree options in SPSS Modeler
2
Building CHAID model and add a second model with C&RT
3
Analysis nodes
4
Lift and gains chart

CHAID

1
What's an algorithm
2
Chi-squared
3
Buliding a tree interactively
4
Bonferonni adjustment and level of measurement
5
CHAID

C&RT

1
Gini coefficient
2
Understanding C&RT
3
The complete C&RT tree
4
Stopping rules in CHAID and C&RT
5
Improving your model

QUEST

1
Understanding QUEST
2
How QUEST handles variables
3
How QUEST handles missing data
4
Pruning and stopping rules in QUEST

C5.0

1
ID3 and C4.5
2
Winnowing attributes and rule sets
3
Understanding information gain
4
Pruning in C5.0
5
How C5.0 handles missing data

Advanced Topics

1
Ensembles
2
Bagging
3
Random forests
4
Boosting
5
Costs and priors
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Includes

2 hours on-demand video
Full lifetime access
Access on mobile and TV
Certificate of Completion