DeepLIFT
DeepLIFT (Deep Learning Important FeaTures) is a feature attribution method for deep neural networks introduced by Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje at Stanford University in 2017 .
Interpretability
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Feature Importances
Feature importances are numeric scores that quantify how much each input feature contributes to the predictions of a machine learning model.
Grad-CAM
Grad-CAM (Gradient-weighted Class Activation Mapping) is a technique for producing visual explanations from convolutional neural network (CNN) models by using the gradients of a target class flowing into the…
Influence functions (machine learning)
An influence function is a tool for estimating how a machine learning model's predictions would change if a single training example were removed or perturbed, without retraining the model.
Integrated Gradients
Integrated Gradients (IG) is a feature-attribution method for explainable AI that explains a neural network prediction by assigning each input feature an importance score
Latent Space
A latent space is the vector space a machine learning model maps its inputs into, where each input becomes a point (a latent vector or latent code) and the geometry of the space carries information the raw…
Layer-wise Relevance Propagation (LRP)
Layer-wise Relevance Propagation (LRP) is an explainable AI method that explains the prediction of a deep neural network by propagating the model's output backward through the network, layer by layer, and…
Permutation variable importances
Permutation variable importance is a model-agnostic technique that measures how much a fitted machine learning model relies on a given feature by randomly shuffling that feature's values and recording how much…
Shapley value
The Shapley value is a rule from cooperative game theory for dividing the payoff of a group among its members.
Sparse Coding
Sparse coding is a representation learning principle in which a signal is encoded as a linear combination of a small number of elements drawn from a (usually overcomplete) dictionary of atoms.
Sparse autoencoder
A sparse autoencoder (SAE) is a neural network that adds a sparsity penalty to an autoencoder's training loss so that only a small number of hidden units activate for any given input, producing a wide
Variable importances
Variable importances, also called feature importances, are scores assigned to each input variable of a predictive model that measure how much that variable contributes to the model's output.