applicable models
supervised
Requires labelled training data
51 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| Mean Decrease Impurity | Algorithmic | Architecture/tree Based Paradigm/supervised +1 | Tabular | Mean Decrease Impurity (MDI) scores how much each feature contributes to a tree-based model, such as a random forest, by... | ||
| Coefficient Magnitudes (in Linear Models) | Metric | Architecture/linear Models Paradigm/parametric +2 | Tabular | Coefficient Magnitudes explains a linear model by reading its fitted coefficients. Each coefficient shows how much the... | ||
| Integrated Gradients | Algorithmic | Architecture/neural Networks Architecture/neural Networks/transformer +4 | Image Tabular Text | Integrated Gradients assigns input features scores for a prediction relative to a chosen reference input, called the... | ||
| Prototype and Criticism Models | Algorithmic | Architecture/model Agnostic Paradigm/supervised +2 | Any | Prototype and criticism methods summarise a dataset with a small set of real examples. Prototypes are instances chosen... | ||
| Influence Functions | Algorithmic | Architecture/linear Models Architecture/neural Networks +5 | Any | Influence functions estimate which training examples most affected a model's prediction on a particular test example.... | ||
| Contrastive Explanation Method | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | The Contrastive Explanation Method (CEM) explains one prediction of a classifier, such as a neural network, by... | ||
| RuleFit | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | RuleFit builds a predictive model that a person can read: a short list of if-then rules plus a few ordinary feature... | ||
| Monte Carlo Dropout | Algorithmic | Architecture/neural Networks Paradigm/probabilistic +4 | Any | Monte Carlo dropout estimates how uncertain a neural network is about each prediction. Dropout randomly switches off... | ||
| Out-of-Distribution Detector for Neural Networks | Algorithmic | Architecture/neural Networks Paradigm/discriminative +3 | Image | ODIN (Out-of-Distribution Detector for Neural Networks) flags inputs that look unlike the data a neural network... | ||
| Demographic Parity Assessment | Metric | Architecture/model Agnostic Paradigm/supervised +1 | Any | Demographic Parity Assessment checks whether a model gives a favourable outcome, such as a loan approval or an interview... | ||
| Adversarial Debiasing | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | Adversarial debiasing trains a model so that its predictions carry as little information as possible about a protected... | ||
| Counterfactual Fairness Assessment | Testing | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Counterfactual Fairness Assessment tests whether a model would have made the same predictions for a person had their... | ||
| Causal Discrimination Testing | Testing | Architecture/model Agnostic Paradigm/supervised +1 | Tabular Text | Causal discrimination testing checks whether a system gives different outputs to otherwise identical inputs because of a... | ||
| Statistical Oversampling Methods | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Tabular | Statistical oversampling methods rebalance a training set by adding synthetic examples of an under-represented category... | ||
| Federated Learning | Algorithmic | Architecture/linear Models Architecture/neural Networks +4 | Any | Federated learning trains one shared model across several parties, such as phones or hospitals, without collecting their... | ||
| Quantile Regression | Algorithmic | Architecture/linear Models/regression Architecture/neural Networks +4 | Tabular | Quantile regression predicts chosen percentiles of an outcome, such as the 10th, 50th and 90th, rather than only its... | ||
| Platt Scaling | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Platt scaling turns a classifier's raw scores into calibrated probabilities, meaning that among the cases given a... | ||
| Temperature Scaling | Algorithmic | Architecture/neural Networks Paradigm/discriminative +4 | Any | Temperature scaling is a correction applied after training to a classifier that is more confident than it is accurate, a... | ||
| Deep Ensembles | Algorithmic | Architecture/neural Networks Paradigm/parametric +2 | Any | Deep ensembles estimate how uncertain a neural network's predictions are by training several copies of the same network... | ||
| Bootstrap Ensembles | Algorithmic | Architecture/model Agnostic Paradigm/supervised +1 | Any | Bootstrap ensembles estimate how much a model's predictions depend on the particular sample of data it was trained on.... |
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