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1 | 1 | # Table of contents |
2 | 2 |
|
| 3 | +* [README](README.md) |
| 4 | + |
3 | 5 | ## Overview |
4 | 6 |
|
5 | | -* [Introduction](source/overview/high_level.md) |
| 7 | +* [Introduction](source/overview/README.md) |
6 | 8 | * [Getting Started](source/overview/getting_started.md) |
7 | 9 | * [Algorithm Overview](source/overview/algorithms.md) |
8 | 10 | * [White-box and black-box models](source/overview/white_box_black_box.md) |
9 | 11 | * [Saving and loading](source/overview/saving.md) |
10 | 12 | * [Frequently Asked Questions](source/overview/faq.md) |
11 | 13 |
|
12 | 14 | ## Explanations |
13 | | -* [Methods](source/methods/README.md) |
14 | | - * [ALE](source/methods/ale.md) |
15 | | - * [Anchors](source/methods/anchors.md) |
16 | | - * [CEM](source/methods/cem.md) |
17 | | - * [CF](source/methods/cf.md) |
18 | | - * [CFProto](source/methods/cfproto.md) |
19 | | - * [CFRL](source/methods/cfrl.md) |
20 | | - * [IntegratedGradients](source/methods/integratedgradients.md) |
21 | | - * [KernelSHAP](source/methods/kernelshap.md) |
22 | | - * [LinearityMeasure](source/methods/linearitymeasure.md) |
23 | | - * [PartialDependence](source/methods/partialdependence.md) |
24 | | - * [PartialDependenceVariance](source/methods/partialdependencevariance.md) |
25 | | - * [PermutationImportance](source/methods/permutationimportance.md) |
26 | | - * [ProtoSelect](source/methods/protoselect.md) |
27 | | - * [Similarity](source/methods/similarity.md) |
28 | | - * [TreeSHAP](source/methods/treeshap.md) |
29 | | - * [TrustScores](source/methods/trustscores.md) |
30 | | -* [Examples](source/methods/README.md) |
31 | | - * [Alibi Overview Examples](source/examples/overview.md) |
32 | | - * [Accumulated Local Effets] |
| 15 | + |
| 16 | +* [Explanations](source/explanations/README.md) |
| 17 | + * [Examples](source/explanations/examples.md) |
| 18 | + * [Methods](source/explanations/methods.md) |
| 19 | + * [methods](source/methods/README.md) |
| 20 | + * [ALE](source/methods/ale.md) |
| 21 | + * [Anchors](source/methods/anchors.md) |
| 22 | + * [CEM](source/methods/cem.md) |
| 23 | + * [CF](source/methods/cf.md) |
| 24 | + * [CFProto](source/methods/cfproto.md) |
| 25 | + * [CFRL](source/methods/cfrl.md) |
| 26 | + * [IntegratedGradients](source/methods/integratedgradients.md) |
| 27 | + * [KernelSHAP](source/methods/kernelshap.md) |
| 28 | + * [LinearityMeasure](source/methods/linearitymeasure.md) |
| 29 | + * [PartialDependence](source/methods/partialdependence.md) |
| 30 | + * [PartialDependenceVariance](source/methods/partialdependencevariance.md) |
| 31 | + * [PermutationImportance](source/methods/permutationimportance.md) |
| 32 | + * [ProtoSelect](source/methods/protoselect.md) |
| 33 | + * [Similarity](source/methods/similarity.md) |
| 34 | + * [TreeSHAP](source/methods/treeshap.md) |
| 35 | + * [TrustScores](source/methods/trustscores.md) |
| 36 | + * [ALE Figures](source/methods/ale_figures.md) |
| 37 | + |
| 38 | +## Model Confidence |
| 39 | + |
| 40 | +* [confidence](source/confidence/README.md) |
| 41 | + * [Examples](source/confidence/examples.md) |
| 42 | + * [Methods](source/confidence/methods.md) |
| 43 | + * [examples](source/examples/README.md) |
33 | 44 | * [Accumulated Local Effects for classifying flowers](source/examples/ale_classification.md) |
34 | 45 | * [Accumulated Local Effects for predicting house prices](source/examples/ale_regression_california.md) |
35 | | - * [Anchors] |
36 | 46 | * [Anchor explanations for fashion MNIST](source/examples/anchor_image_fashion_mnist.md) |
37 | 47 | * [Anchor explanations for ImageNet](source/examples/anchor_image_imagenet.md) |
38 | 48 | * [Anchor explanations for income prediction](source/examples/anchor_tabular_adult.md) |
39 | 49 | * [Anchor explanations on the Iris dataset](source/examples/anchor_tabular_iris.md) |
40 | 50 | * [Anchor explanations for movie sentiment](source/examples/anchor_text_movie.md) |
41 | | - * [Contrastive Explanation Method] |
42 | 51 | * [Contrastive Explanations Method (CEM) applied to Iris dataset](source/examples/cem_iris.md) |
43 | 52 | * [Contrastive Explanations Method (CEM) applied to MNIST](source/examples/cem_mnist.md) |
44 | | - * [Counterfactual Instances on MNIST](source/examples/cf_mnist.md) |
45 | | - * [Counterfactuals Guided by Prototypes] |
| 53 | + * [Counterfactual instances on MNIST](source/examples/cf_mnist.md) |
46 | 54 | * [Counterfactual explanations with one-hot encoded categorical variables](source/examples/cfproto_cat_adult_ohe.md) |
47 | 55 | * [Counterfactual explanations with ordinally encoded categorical variables](source/examples/cfproto_cat_adult_ord.md) |
48 | 56 | * [Counterfactuals guided by prototypes on California housing dataset](source/examples/cfproto_housing.md) |
49 | 57 | * [Counterfactuals guided by prototypes on MNIST](source/examples/cfproto_mnist.md) |
50 | | - * [Counterfactuals with Reinforcement Learning] |
51 | 58 | * [Counterfactual with Reinforcement Learning (CFRL) on Adult Census](source/examples/cfrl_adult.md) |
52 | 59 | * [Counterfactual with Reinforcement Learning (CFRL) on MNIST](source/examples/cfrl_mnist.md) |
53 | | - * [Integrated Gradients] |
| 60 | + * [Distributed KernelSHAP](source/examples/distributed_kernel_shap_adult_lr.md) |
54 | 61 | * [Integrated gradients for a ResNet model trained on Imagenet dataset](source/examples/integrated_gradients_imagenet.md) |
55 | 62 | * [Integrated gradients for text classification on the IMDB dataset](source/examples/integrated_gradients_imdb.md) |
56 | 63 | * [Integrated gradients for MNIST](source/examples/integrated_gradients_mnist.md) |
57 | 64 | * [Integrated gradients for transformers models](source/examples/integrated_gradients_transformers.md) |
58 | | - * [Kernel SHAP] |
59 | | - * [Distributed KernelSHAP](source/examples/distributed_kernel_shap_adult_lr.md) |
| 65 | + * [Explaining Tree Models with Interventional Feature Perturbation Tree SHAP](source/examples/interventional_tree_shap_adult_xgb.md) |
60 | 66 | * [KernelSHAP: combining preprocessor and predictor](source/examples/kernel_shap_adult_categorical_preproc.md) |
61 | 67 | * [Handling categorical variables with KernelSHAP](source/examples/kernel_shap_adult_lr.md) |
62 | 68 | * [Kernel SHAP explanation for SVM models](source/examples/kernel_shap_wine_intro.md) |
63 | 69 | * [Kernel SHAP explanation for multinomial logistic regression models](source/examples/kernel_shap_wine_lr.md) |
64 | | - * [Partial Dependence] |
65 | | - * [Partial Dependence and Individual Conditional Expectation for predicting bike renting](source/examples/pdp_regression_bike.md) |
66 | | - * [Partial Dependence Variance] |
| 70 | + * [Linearity measure applied to fashion MNIST](source/examples/linearity_measure_fashion_mnist.md) |
| 71 | + * [Linearity measure applied to Iris](source/examples/linearity_measure_iris.md) |
| 72 | + * [Alibi Overview Example](source/examples/overview.md) |
| 73 | + * [Explaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP](source/examples/path_dependent_tree_shap_adult_xgb.md) |
67 | 74 | * [Feature importance and feature interaction based on partial dependece variance](source/examples/pd_variance_regression_friedman.md) |
68 | | - * [Permutation Importance] |
69 | | - * [Permutation Feature Importance on “Who’s Going to Leave Next?”](source/examples/permutation_importance_classification_leave.md) |
70 | | - * [Similarity explanations] |
| 75 | + * [Partial Dependence and Individual Conditional Expectation for predicting bike renting](source/examples/pdp_regression_bike.md) |
| 76 | + * [Permutation Feature Importance on "Who's Going to Leave Next?"](source/examples/permutation_importance_classification_leave.md) |
| 77 | + * [ProtoSelect on Adult Census and CIFAR10](source/examples/protoselect_adult_cifar10.md) |
71 | 78 | * [Similarity explanations for 20 newsgroups dataset](source/examples/similarity_explanations_20ng.md) |
72 | 79 | * [Similarity explanations for ImageNet](source/examples/similarity_explanations_imagenet.md) |
73 | 80 | * [Similarity explanations for MNIST](source/examples/similarity_explanations_mnist.md) |
74 | | - * [Tree SHAP] |
75 | | - * [Explaining Tree Models with Interventional Feature Perturbation Tree SHAP](source/examples/interventional_tree_shap_adult_xgb.md) |
76 | | - * [Explaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP](source/examples/path_dependent_tree_shap_adult_xgb.md) |
77 | | - |
78 | | -## Model Confidence |
79 | | - |
80 | | -* [Methods] |
81 | | - * [Measuring the linearity of machine learning models](source/methods/linearitymeasure.md) |
82 | | - * [Trust Scores](source/methods/trustscores.md) |
83 | | -* [Examples] |
84 | | - * [Measuring the linearity of machine learning models] |
85 | | - * [Linearity measure applied to fashion MNIST](source/examples/linearity_measure_fashion_mnist.md) |
86 | | - * [Linearity measure applied to Iris](source/examples/linearity_measure_iris.md) |
87 | | - * [Trust Scores] |
88 | 81 | * [Trust Scores applied to Iris](source/examples/trustscore_iris.md) |
89 | 82 | * [Trust Scores applied to MNIST](source/examples/trustscore_mnist.md) |
| 83 | + * [A Gradient Boosted Tree Model for the Adult Dataset](source/examples/xgboost_model_fitting_adult.md) |
| 84 | + * [Methods](model-confidence/confidence/methods-1.md) |
| 85 | + * [Examples](model-confidence/confidence/examples-2.md) |
90 | 86 |
|
91 | 87 | ## Prototypes |
92 | 88 |
|
93 | 89 | * [prototypes](source/prototypes/README.md) |
94 | | - * [Methods](source/prototypes/methods.md) |
95 | 90 | * [Examples](source/prototypes/examples.md) |
96 | | - |
| 91 | + * [Methods](source/prototypes/methods.md) |
97 | 92 |
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98 | 93 | ## API Reference |
99 | 94 |
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