The End Of Knowledge - Vault 6/55 - CVPR - 2020 - Explaining Tree-based Machine Learning Models

graph LR classDef main fill:#f9d4f9, font-weight:bold, font-size:14px classDef tree fill:#f9d4d4, font-weight:bold, font-size:14px classDef shap fill:#d4f9d4, font-weight:bold, font-size:14px classDef gan fill:#d4d4f9, font-weight:bold, font-size:14px classDef latent fill:#f9f9d4, font-weight:bold, font-size:14px classDef interpretation fill:#d4f9f9, font-weight:bold, font-size:14px Main[Explaining Tree-based Machine
Learning Models] --> A[Tree-based Models] Main --> B[SHAP Explanations] Main --> C[GAN Basics] Main --> D[GAN Latent Space] Main --> E[GAN Interpretation] A --> A1[Tree-based model explanation is challenging 1] A --> A2[Feature importance depends on tree
structure 2] A --> A3[Shapley values measure feature importance 3] A --> A4[TreeSHAP reduces Shapley value complexity 4] A --> A5[Complex models capture non-linear relationships
better 7] A --> A6[Linear models misassign weights to
irrelevant features 8] B --> B1[SHAP explains datasets, global model
structure 5] B --> B2[SHAP relates to partial dependence
plots 6] B --> B3[SHAP summary plots show feature
importance 9] B --> B4[SHAP interaction plots reveal feature
interactions 10] B --> B5[SHAP explains model loss for
monitoring 11] B --> B6[SHAP-based monitoring detects data drift 12] C --> C1[GANs synthesize diverse photorealistic images 13] C --> C2[GAN dissection interprets internal unit
functions 14] C --> C3[GANs: generator and discriminator train
adversarially 19] C --> C4[GAN semantics: internal units, latent
space 20] C --> C5[Semantic segmentation labels internal GAN
units 21] C --> C6[GAN units specialize in specific
objects/textures 22] D --> D1[GAN latent space controls image
semantics 15] D --> D2[Random walks reveal smooth attribute
transitions 16] D --> D3[Latent space encodes various image
attributes 24] D --> D4[Visualizing walks reveals attribute transitions 25] D --> D5[Initial latent space drives image
diversity 29] D --> D6[Latent transitions reveal encoded semantic
concepts 30] E --> E1[GAN dissection enables interactive image
editing 17] E --> E2[Interpreting generative models explains image
synthesis 18] E --> E3[Controlling units adds/removes image content 23] E --> E4[Interpreting GANs reveals learned image
compositions 26] E --> E5[Dissection correlates activations with semantic
segmentation 27] E --> E6[Manipulating semantic units enables image
editing 28] class Main main class A,A1,A2,A3,A4,A5,A6 tree class B,B1,B2,B3,B4,B5,B6 shap class C,C1,C2,C3,C4,C5,C6 gan class D,D1,D2,D3,D4,D5,D6 latent class E,E1,E2,E3,E4,E5,E6 interpretation

Resume:

1.- Explaining tree-based machine learning models is challenging, even for simple decision trees.

2.- Measuring feature importance in decision trees is tricky and often depends on tree structure.

3.- Shapley values from game theory provide a desirable way to measure feature importance in decision trees.

4.- TreeSHAP reduces exponential complexity of Shapley values to polynomial time for tree-based models.

5.- SHAP values can be used to explain entire datasets and represent global model structure.

6.- SHAP values are closely related to partial dependence plots for simple models.

7.- Complex models can capture non-linear relationships better than high-bias linear models.

8.- Linear models may assign weight to irrelevant features when applied to non-linear data.

9.- SHAP values can be used to create summary plots showing feature importance and interactions.

10.- SHAP interaction plots reveal how features interact to affect model predictions.

11.- SHAP values can be used to explain model loss, useful for model monitoring.

12.- SHAP-based monitoring can detect subtle data drift and bugs that affect model performance.

13.- Generative Adversarial Networks (GANs) can synthesize photorealistic images with diverse characteristics.

14.- GAN dissection interprets internal units of generators as object synthesizers.

15.- Latent space of GANs controls various semantic aspects of generated images.

16.- Random walks in GAN latent space reveal smooth transitions between different image attributes.

17.- GAN dissection allows interactive editing of synthesized images by manipulating internal units.

18.- Interpreting deep generative models helps understand how they synthesize realistic images.

19.- GANs consist of a generator and discriminator trained adversarially.

20.- Latent semantics in GANs include internal units and the initial latent space.

21.- Semantic segmentation helps associate labels with internal GAN units.

22.- GAN dissection identifies units specialized for synthesizing specific objects or textures.

23.- Controlling GAN units allows adding or removing specific content in generated images.

24.- The latent space of GANs encodes various semantic attributes of generated images.

25.- Visualizing random walks in GAN latent space reveals smooth transitions between image attributes.

26.- Interpreting GANs helps understand learned image compositions and content representations.

27.- GAN dissection correlates unit activations with semantic segmentation to identify unit functions.

28.- Interactive editing of GAN-generated images is possible by manipulating identified semantic units.

29.- The initial latent space of GANs is the primary driver for synthesizing diverse images.

30.- Visualizing GAN latent space transitions reveals encoded semantic concepts like color, layout, and object presence.

Knowledge Vault built byDavid Vivancos 2024