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Interpretable machine learning
The document discusses machine learning interpretability, focusing on visualization techniques and analysis methods such as glyphs, correlation graphs, and residual analysis. It addresses the application of machine learning in regulated industries and explores understanding complex models through surrogate models, LIME, and variable importance measures. Overall, it emphasizes the importance of transparent and dependable interpretations in machine learning applications.
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Interpretable machine learning
- 1. Ideas on MachineLearning Interpretability Patrick Hall, Wen Phan, SriSatish Ambati and the H2O.ai team February 2017
- 2. 2 Part 1: Seeingyour data • Glyphs • Correlation graphs • 2-D projections • Partial dependence plots • Residual analysis Part 2: Using machine learning in regulated industry • OLS regression alternatives • Build toward ML model benchmarks • ML in traditional analytics processes • Small, interpretable ensembles • Monotonicity constraints Part 3: Understanding complex ML models • Surrogate models • LIME • Maximum activation analysis • Sensitivity analysis • Variable importance measures • TreeInterpreter Contents
- 3. 3 A framework forinterpretability Complexity of learned functions: • Linear, monotonic • Nonlinear, monotonic • Nonlinear, non-monotonic Enhancing trust and understanding: the mechanisms and results of an interpretable model should be both transparent AND dependable. Scope of interpretability: Global vs. local Application domain: Model-agnostic vs. model-specific
- 4. Part 1: Seeingyour data
- 5. 5 Glyphs Variables and theirvalues can be represented by small pictures with certain attributes, called “glyphs”. Here the four variables are represented by their position in a square and their values are represented by a color. os_type os_version iphone newest oldest OSX Win Android Linux agent_type agent_version Opera newest oldest Safari IE Others Firefox bot agent_type agent_version os_type os_version
- 6. 6 Correlation graphs The nodesof this graph are the variables in a data set. The weights between the nodes are defined by the absolute value of their pairwise Pearson correlation.
- 7. 7 2-D projections 784 dimensionsto 2 dimensions with PCA 784 dimensions to 2 dimensions with autoencoder network
- 8. 8 Partial dependence plots Source:http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf HomeValue ~ MedInc + AveOccup + HouseAge + AveRooms
- 9. 9 Residual analysis Residuals froma machine learning model should be randomly distributed obvious patterns in residuals can indicate problems with data preparation or model specification
- 10. Part 2: UsingML in regulated industry
- 11. 11 OLS regression alternatives PenalizedRegression Generalized Additive Models Quantile Regression Source: http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
- 12. 12 Build toward MLmodel benchmarks Incorporate interactions and piecewise linear components to increase the accuracy of linear models relative to machine learning models.
- 13. 13 Machine learning intraditional analytics processes
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- 16. Part 3: Understandingcomplex machine learning models
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- 18. 18 Local interpretable model-agnosticexplanations Source: https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime
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- 20. 20 Sensitivity analysis Data distributionsshift over time. How will your model handle these shifts?
- 21. 21 Variable importance measures Globalvariable importance indicates the impact of a variable on the model for the entire training data set. Local variable importance can indicate the impact of a variable for each decision a model makes – similar to reason codes.
- 22. 22 Treeinterpreter Source: http://blog.datadive.net/interpreting-random-forests/ Tree interpreterdecomposes decision tree and random forest predictions into bias (overall average) and component terms. This slide portrays the decomposition of the decision path into bias and individual contributions for a single decision tree - treeinterpreter simply prints a ranked list of the bias and individual contributions for a given prediction.