Feature Importance

240 papers with code • 6 benchmarks • 5 datasets

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Libraries

Use these libraries to find Feature Importance models and implementations

Most implemented papers

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

smerdov/eSports_Sensors_Dataset 2 Nov 2020

An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level.

Feature Importance-aware Transferable Adversarial Attacks

hcguoO0/FIA ICCV 2021

More specifically, we obtain feature importance by introducing the aggregate gradient, which averages the gradients with respect to feature maps of the source model, computed on a batch of random transforms of the original clean image.

Label-Free Explainability for Unsupervised Models

vanderschaarlab/mlforhealthlabpub 3 Mar 2022

Unsupervised black-box models are challenging to interpret.

Interpretable machine learning for time-to-event prediction in medicine and healthcare

modeloriented/survex 17 Mar 2023

Time-to-event prediction, e. g. cancer survival analysis or hospital length of stay, is a highly prominent machine learning task in medical and healthcare applications.

Interpretation of Neural Networks is Fragile

pytorch/captum 29 Oct 2017

In this paper, we show that interpretation of deep learning predictions is extremely fragile in the following sense: two perceptively indistinguishable inputs with the same predicted label can be assigned very different interpretations.

Towards Automatic Concept-based Explanations

amiratag/ACE NeurIPS 2019

Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions.

Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations

koalaverse/vip 8 Apr 2019

Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model.

Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review

bghojogh/Feature-Extraction-Survey 7 May 2019

Pattern analysis often requires a pre-processing stage for extracting or selecting features in order to help the classification, prediction, or clustering stage discriminate or represent the data in a better way.

Computationally Efficient Feature Significance and Importance for Machine Learning Models

fintechstanford/SFIT 23 May 2019

We develop a simple and computationally efficient significance test for the features of a machine learning model.

Benchmarking Attribution Methods with Relative Feature Importance

google-research-datasets/bim 23 Jul 2019

Despite active development, quantitative evaluation of feature attribution methods remains difficult due to the lack of ground truth: we do not know which input features are in fact important to a model.