Fundamentals of Decision Trees in Machine Learning
Fundamentals of Decision Trees in Machine Learning Download
What you’ll 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
Requirements
- Basic understanding of statistics
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.
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.
Who this course is for:
- Anyone interested in learning machine learning
- Data science specialists
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