Expand your data science skills and establish a strong foundation in codeless machine learning.
Machine Learning and AI Foundations: Decision Trees with KNIME
Overview
Syllabus
Introduction
- The basics of decision trees
- What you should know
- How to use the practice files
- What is a decision tree?
- The pros and cons of decision trees
- Introducing KNIME
- A quick review of machine learning basics with examples
- An overview of decision tree algorithms
- Ross Quinlan, ID3, C4.5, and C5.0
- Understanding the entropy calculation
- How C4.5 handles missing data
- The Give Me Some Credit data set
- Working with the prebuilt example
- KNIME settings for C4.5
- How C4.5 handles nominal variables
- How C4.5 handles continuous variables
- Equal size sampling
- A quick look at the complete C4.5 tree
- Evaluating the accuracy of your C4.5 tree
- When to turn off pruning
- Introducing Leo Breiman and CART
- What is the Gini coefficient?
- How CART handles missing data using surrogates
- Changing the settings in KNIME
- How CART handles nominal variables
- A quick look at the complete CART tree
- Evaluating the accuracy of your CART tree
- MPG data set
- The regression tree prebuilt example
- The math behind regression trees
- How RT handles nominal variables
- Ordinal variable handling
- Closer look at a full regression tree
- KNIME's missing data options for regression trees
- Line plot
- Accuracy
- Next steps
Taught by
Keith McCormick
