Permutation Importance

Explainability Reliability

Description

Permutation Importance quantifies a feature's contribution to a model's performance by randomly shuffling its values and measuring the resulting drop in predictive accuracy. If shuffling a feature significantly degrades the model's performance, that feature is considered important. This model-agnostic technique helps identify which inputs are genuinely driving predictions, rather than just being correlated with the outcome.

Example Use Cases

Explainability

Assessing which patient characteristics (e.g., age, blood pressure, cholesterol) are most critical for a medical diagnosis model by observing the performance drop when each characteristic's values are randomly shuffled, ensuring the model relies on clinically relevant factors.

Reliability

Validating the robustness of a fraud detection model by permuting features like transaction amount or location, and confirming that the model's ability to detect fraud significantly decreases only for truly important features, thereby improving confidence in its reliability.

Limitations

Can be misleading when features are highly correlated, as shuffling one feature might indirectly affect others, leading to an overestimation of its importance.
Computationally expensive for large datasets or complex models, as it requires re-evaluating the model many times for each feature.
Does not account for interactions between features; it measures the marginal importance of a feature, assuming other features remain unchanged.
The choice of metric for evaluating performance drop (e.g., accuracy, F1-score) can influence the perceived importance of features.

Resources

Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models

Research Paper•Burim Ramosaj and Markus Pauly•Dec 5, 2019

eli5.permutation_importance — ELI5 0.15.0 documentation

Documentation

Permutation Importance — PermutationImportance 1.2.1.5 ...

Documentation

parrt/random-forest-importances

Software Package

Statistically Valid Variable Importance Assessment through Conditional Permutations

Research Paper•Ahmad Chamma, Denis A. Engemann, and Bertrand Thirion•Sep 14, 2023

Related Techniques

Name	Description	Assurance Goals
Local Interpretable Model-Agnostic Explanations	LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex model's behaviour in a small neighbourhood around a specific instance. It works by creating perturbed versions of the input (e.g., removing words from text, changing pixel values in images, or varying feature values), obtaining the model's predictions for these variations, and training a simple interpretable model (typically linear regression) weighted by proximity to the original instance. The coefficients of this local surrogate model reveal which features most influenced the specific prediction.	Explainability Transparency
ANCHOR	ANCHOR generates high-precision if-then rules that explain individual predictions by identifying the minimal set of feature conditions that guarantee a specific prediction with high confidence. It searches for 'anchor' conditions (e.g., 'age > 30 AND income < £50k') that ensure the model gives the same prediction at least 95% of the time when those conditions are met. This creates human-readable rules that users can trust as sufficient conditions for understanding why a particular decision was made.	Explainability Transparency
Deep Ensembles	Deep ensembles combine predictions from multiple neural networks trained independently with different random initializations to capture epistemic uncertainty (model uncertainty). By training several models on the same data with different starting points, the ensemble reveals how much the model's predictions depend on training randomness. The disagreement between ensemble members naturally indicates prediction uncertainty - when models agree, confidence is high; when they disagree, uncertainty is revealed. This approach provides more reliable uncertainty estimates, better out-of-distribution detection, and improved calibration compared to single models.	Reliability Transparency Safety
UMAP	UMAP (Uniform Manifold Approximation and Projection) is a non-linear dimensionality reduction technique that creates 2D or 3D visualisations of high-dimensional data by constructing a mathematical model of the data's underlying manifold structure. Unlike t-SNE, UMAP preserves both local neighbourhood relationships and global topology more effectively, using techniques from topological data analysis and Riemannian geometry. This approach often produces more interpretable cluster layouts while maintaining meaningful distances between clusters, making it particularly valuable for exploratory data analysis and understanding complex dataset structures.	Explainability
Prediction Intervals	Prediction intervals provide a range of plausible values around a model's prediction, expressing uncertainty as 'the true value will likely fall between X and Y with Z% confidence'. For example, instead of predicting 'house price: £300,000', a prediction interval might say 'house price: £280,000 to £320,000 with 95% confidence'. This technique works by calculating upper and lower bounds that account for both model uncertainty (how confident the model is) and inherent randomness in the data. Prediction intervals are crucial for informed decision-making, as they help users understand the reliability and precision of predictions, enabling better risk assessment and planning.	Reliability Transparency Fairness
Taylor Decomposition	Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and higher-order derivatives of the network's output with respect to input features. It decomposes the prediction into relevance scores that indicate how much each input feature and feature interaction contributes to the final decision. The method uses Layer-wise Relevance Propagation (LRP) principles to trace prediction contributions backwards through the network layers, providing precise mathematical attributions for each input element.	Explainability

Tags

Applicable Models:

Assurance Goal Category:

Importance And Attribution

Data Requirements:

No Special Requirements

Data Type:

Evidence Type:

Quantitative Metric

Expertise Needed:

Explanatory Scope:

Lifecycle Stage:

Model Development

Technique Type: