expertise needed
statistics
Requires knowledge of statistical methods and analysis
81 techniques
| Goals | Models | Data Types | Description | |||
|---|---|---|---|---|---|---|
| SHapley Additive exPlanations | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | SHAP (SHapley Additive exPlanations) explains one prediction by sharing out the gap between that prediction and a... | ||
| Permutation Importance | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Permutation Importance measures how much a trained model relies on each input feature. For each feature, the values in a... | ||
| 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... | ||
| Sobol Indices | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | Sobol indices show how much of the variation in a model's output can be traced to each of its inputs. The analyst sets a... | ||
| Partial Dependence Plots | Visualisation | Architecture/model Agnostic Requirements/black Box | Tabular | Partial Dependence Plots show how changing one or two features affects a model's predictions on average. The technique... | ||
| Individual Conditional Expectation Plots | Visualisation | Architecture/model Agnostic Requirements/black Box | Tabular | Individual Conditional Expectation (ICE) plots display the predicted output for individual instances as a function of a... | ||
| Factor Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Tabular | Factor analysis looks for a small number of hidden factors that explain why a larger set of measured variables, such as... | ||
| Principal Component Analysis | Algorithmic | Architecture/model Agnostic Paradigm/unsupervised +1 | Any | Principal component analysis (PCA) summarises a dataset that has many numeric features by finding a few new axes along... | ||
| t-SNE | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | t-SNE (t-distributed Stochastic Neighbour Embedding) turns high-dimensional data into a two- or three-dimensional map... | ||
| UMAP | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction method that turns... | ||
| ANCHOR | Algorithmic | Architecture/model Agnostic Requirements/black Box | Tabular | ANCHOR explains one prediction of a black-box classifier with an if-then rule, called an anchor, such as 'age > 30 AND... | ||
| 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... | ||
| Permutation Tests | Algorithmic | Architecture/model Agnostic Requirements/black Box | Any | A permutation test asks how likely an observed result would be if there were no real relationship in the data. The... | ||
| 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... | ||
| 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... | ||
| GAN-Based Tabular Synthetic Data | Algorithmic | Architecture/neural Networks/generative/gan Architecture/neural Networks/generative/vae +3 | Tabular | GAN-based tabular synthetic data generation trains a generative model on a real table, then samples new rows from it to... |
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