Learn the fundamentals of decision trees in machine learning
Using the SPSS Modeler
Building a CHAID model
Using a lift and gains chart
Building a tree interactively
7 sections • 31 lectures • 2h 27m total length
Building CHAID model and add a second model with C&RT
Lift and gains chart
Buliding a tree interactively
Bonferonni adjustment and level of measurement
The complete C&RT tree
Stopping rules in CHAID and C&RT
Improving your model
How QUEST handles variables
How QUEST handles missing data
Pruning and stopping rules in QUEST
ID3 and C4.5
Winnowing attributes and rule sets
Understanding information gain
Pruning in C5.0
How C5.0 handles missing data
Basic understanding of statistics
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.