expertise needed

ml engineering

Requires machine learning engineering skills

57 techniques

		Models	Data Types	Description
Integrated Gradients	Algorithmic	Neural Network	Any	Integrated Gradients is an attribution technique that explains a model's prediction by quantifying the contribution of...
DeepLIFT	Algorithmic	Neural Network	Any	DeepLIFT (Deep Learning Important FeaTures) explains neural network predictions by decomposing the difference between...
Layer-wise Relevance Propagation	Algorithmic	Neural Network	Any	Layer-wise Relevance Propagation (LRP) explains neural network predictions by working backwards through the network to...
Contextual Decomposition	Algorithmic	Recurrent Neural Network	Text	Contextual Decomposition explains LSTM and RNN predictions by decomposing the final hidden state into contributions from...
Taylor Decomposition	Algorithmic	Neural Network CNN	Any	Taylor Decomposition is a mathematical technique that explains neural network predictions by computing first-order and...
Sobol Indices	Algorithmic	Model Agnostic	Any	Sobol Indices quantify how much each input feature contributes to the total variance in a model's predictions through...
Local Interpretable Model-Agnostic Explanations	Algorithmic	Model Agnostic	Any	LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions by approximating the complex...
Ridge Regression Surrogates	Algorithmic	Model Agnostic	Any	This technique approximates a complex model by training a ridge regression (a linear model with L2 regularization) on...
Partial Dependence Plots	Algorithmic	Model Agnostic	Any	Partial Dependence Plots show how changing one or two features affects a model's predictions on average. The technique...
Saliency Maps	Algorithmic	Neural Network	Image	Saliency maps are visual explanations for image classification models that highlight which pixels in an image most...
Gradient-weighted Class Activation Mapping	Algorithmic	CNN	Image	Grad-CAM creates visual heatmaps showing which regions of an image a convolutional neural network focuses on when making...
Occlusion Sensitivity	Algorithmic	Model Agnostic	Image	Occlusion sensitivity tests which parts of the input are important by occluding (masking or removing) them and seeing...
Classical Attention Analysis in Neural Networks	Algorithmic	Rnn CNN	Any	Classical attention mechanisms in RNNs and CNNs create alignment matrices and temporal attention patterns that show how...
Prototype and Criticism Models	Algorithmic	Model Agnostic	Any	Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes...
Influence Functions	Algorithmic	Model Agnostic	Any	Influence functions quantify how much each training example influenced a model's predictions by computing the change in...
Contrastive Explanation Method	Algorithmic	Model Agnostic	Any	The Contrastive Explanation Method (CEM) explains model decisions by generating contrastive examples that reveal what...
Monte Carlo Dropout	Algorithmic	Neural Network	Any	Monte Carlo Dropout estimates prediction uncertainty by applying dropout (randomly setting neural network weights to...
Out-of-DIstribution detector for Neural networks	Algorithmic	Neural Network	Any	ODIN (Out-of-Distribution Detector for Neural Networks) identifies when a neural network encounters inputs significantly...
Adversarial Debiasing	Algorithmic	Neural Network	Any	Adversarial debiasing reduces bias by training models using a competitive adversarial setup, similar to Generative...
Counterfactual Fairness Assessment	Algorithmic	Model Agnostic	Any	Counterfactual Fairness Assessment evaluates whether a model's predictions would remain unchanged if an individual's...

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