applicable models
white box
Requires full model transparency and access
35 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Mean Decrease Impurity | Algorithmic | Architecture/tree Based Paradigm/supervised +1 | Tabular | Mean Decrease Impurity (MDI) quantifies a feature's importance in tree-based models (e.g., Random Forests, Gradient... | ||
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients.... | ||
| DeepLIFT | Algorithmic | Architecture/neural Networks Requirements/white Box +1 | Any | DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between... | ||
| Layer-wise Relevance Propagation | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to... | ||
| Contextual Decomposition | Algorithmic | Architecture/neural Networks/recurrent Requirements/white Box +1 | Text | Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from... | ||
| Taylor Decomposition | Algorithmic | Architecture/neural Networks Requirements/gradient Access +2 | Any | Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and... | ||
| Gradient-weighted Class Activation Mapping | Algorithmic | Architecture/neural Networks/convolutional Requirements/architecture Specific +2 | Image | Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +6 | Any | Influence functions quantify how much each training example influenced a model's predictions by computing the change in... | ||
| Contrastive Explanation Method | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what... | ||
| Out-of-Distribution Detector for Neural Networks | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Any | ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly... | ||
| Adversarial Debiasing | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | Adversarial debiasing reduces bias by training models using a competitive adversarial setup, similar to Generative... | ||
| Synthetic Data Generation | Algorithmic | Architecture/neural Networks/generative/gan Architecture/neural Networks/generative/vae +5 | Any | Synthetic data generation creates artificial datasets that aim to preserve the statistical properties, distributions,... | ||
| Federated Learning | Algorithmic | Architecture/linear Models Architecture/neural Networks +4 | Any | Federated learning enables collaborative model training across multiple distributed parties (devices, organisations, or... | ||
| Homomorphic Encryption | Algorithmic | Architecture/linear Models Architecture/neural Networks/feedforward +4 | Any | Homomorphic encryption allows computation on encrypted data without decrypting it first, producing encrypted results... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Any | Quantile regression estimates specific percentiles (quantiles) of the target variable rather than just predicting the... | ||
| Temperature Scaling | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Any | Temperature scaling adjusts a model's confidence by applying a single parameter (temperature) to its predictions. When a... | ||
| Model Distillation | Algorithmic | Architecture/neural Networks Paradigm/parametric +3 | Any | Model distillation transfers knowledge from a large, complex model (teacher) to a smaller, more efficient model... | ||
| Monotonicity Constraints | Algorithmic | Architecture/probabilistic/gaussian Processes Architecture/tree Based +2 | Tabular | Monotonicity constraints enforce consistent directional relationships between input features and model predictions,... | ||
| Intrinsically Interpretable Models | Algorithmic | Architecture/linear Models Architecture/tree Based +2 | Any | Intrinsically interpretable models are machine learning algorithms that are transparent by design, allowing users to... | ||
| Generalized Additive Models | Algorithmic | Architecture/linear Models/gam Paradigm/parametric +2 | Tabular | An intrinsically interpretable modelling technique that extends linear models by allowing flexible, nonlinear... |
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