Machine Learning and AI Foundations: Decision Trees with KNIME
2hIntermediate2022-06-22
Authors
Keith McCormick
Data Miner, Trainer, Speaker, Author
Course details
Many data science specialists are looking to pivot toward focusing on machine learning. In this course, Keith McCormick covers the essentials of machine learning pertaining to predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the KNIME modeler are included so you can understand how decision trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.
Skills covered
KNIMEDecision-MakingMachine LearningArtificial Intelligence FoundationsData AnalysisArtificial Intelligence (AI)Data ScienceProfessional DevelopmentBusiness Analysis and StrategyLeadership and ManagementBusiness Software and ToolsDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - The basics of decision trees
- 02 - What you should know
- 03 - How to use the practice files
1. Introducing Decision Trees
- 04 - What is a decision tree
- 05 - The pros and cons of decision trees
- 06 - Introducing KNIME
- 07 - A quick review of machine learning basics with examples
- 08 - An overview of decision tree algorithms
2. Introducing the C5.0 Algorithm
- 09 - Ross Quinlan, ID3, C4.5, and C5.0
- 10 - Understanding the entropy calculation
- 11 - How C4.5 handles missing data
- 12 - The Give Me Some Credit data set
- 13 - Working with the prebuilt example
- 14 - KNIME settings for C4.5
- 15 - How C4.5 handles nominal variables
- 16 - How C4.5 handles continuous variables
- 17 - Equal size sampling
- 18 - A quick look at the complete C4.5 tree
- 19 - Evaluating the accuracy of your C4.5 tree
- 20 - When to turn off pruning
3. Introducing Classification Trees
- 21 - Introducing Leo Breiman and CART
- 22 - What is the Gini coefficient
- 23 - How CART handles missing data using surrogates
- 24 - Changing the settings in KNIME
- 25 - How CART handles nominal variables
- 26 - A quick look at the complete CART tree
- 27 - Evaluating the accuracy of your CART tree
4. Introducing Regression Trees
- 28 - MPG data set
- 29 - The regression tree prebuilt example
- 30 - The math behind regression trees
- 31 - How RT handles nominal variables
- 32 - Ordinal variable handling
- 33 - Closer look at a full regression tree
- 34 - KNIME's missing data options for regression trees
- 35 - Line plot
- 36 - Accuracy
Conclusion
- 37 - Next steps