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Visualize probability Workflows
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Classroom Contents
Visualization Best Practices for Explainable AI
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- 1 Intro
- 2 Technical Assistance
- 3 Machine Learning vs. Artificial Intelligence (AI)
- 4 Why do they call it "Machine Learning"? Machines learn from data to make predictions on new data
- 5 Two stage machine learning process How does it work?
- 6 Automated Machine Learning (AutoML) 3 simple steps
- 7 Explain results Need to speak data using a common language
- 8 Explainable Al, is it possible?
- 9 Helps avoid incompleteness issues Explanations fundamentally help identify gaps in problem formalization - incompleteness
- 10 Visualize how models work
- 11 How to understand a model
- 12 What data did the model use? Understand model data source limitations
- 13 Investigate model training data
- 14 Understand what matters
- 15 Explore relationships between variables
- 16 Examine decision rules Rules Fit Classifiers
- 17 Discover business rules from text fields
- 18 Analyze prediction explanations Global
- 19 Evaluate model performance
- 20 Understand how well a model fits the data
- 21 Examine where models make mistakes
- 22 Detect model changes over time
- 23 Time series
- 24 Visualize probability Workflows
- 25 Resources
