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Feature Importance

35 papers with code · Methodology

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A Unified Approach to Interpreting Model Predictions

NeurIPS 2017 slundberg/shap

Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications.

FEATURE IMPORTANCE INTERPRETABLE MACHINE LEARNING

Distributed and parallel time series feature extraction for industrial big data applications

25 Oct 2016blue-yonder/tsfresh

This problem is especially hard to solve for time series classification and regression in industrial applications such as predictive maintenance or production line optimization, for which each label or regression target is associated with several time series and meta-information simultaneously.

FEATURE IMPORTANCE FEATURE SELECTION REGRESSION TIME SERIES TIME SERIES CLASSIFICATION

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

23 May 2019shenweichen/DeepCTR

In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.

CLICK-THROUGH RATE PREDICTION FEATURE IMPORTANCE REGRESSION

Gradients of Counterfactuals

8 Nov 2016kundajelab/deeplift

Unfortunately, in nonlinear deep networks, not only individual neurons but also the whole network can saturate, and as a result an important input feature can have a tiny gradient.

FEATURE IMPORTANCE LANGUAGE MODELLING OBJECT RECOGNITION

Attention is not Explanation

NAACL 2019 successar/AttentionExplanation

Attention mechanisms have seen wide adoption in neural NLP models.

FEATURE IMPORTANCE

Towards Automatic Concept-based Explanations

NeurIPS 2019 amiratag/ACE

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

FEATURE IMPORTANCE

Interpretable machine learning: definitions, methods, and applications

14 Jan 2019csinva/hierarchical-dnn-interpretations

Official code for using / reproducing ACD (ICLR 2019) from the paper "Hierarchical interpretations for neural network predictions" https://arxiv. org/abs/1806. 05337

FEATURE IMPORTANCE INTERPRETABLE MACHINE LEARNING

Hierarchical interpretations for neural network predictions

ICLR 2019 csinva/hierarchical-dnn-interpretations

Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables.

FEATURE IMPORTANCE INTERPRETABLE MACHINE LEARNING

Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

10 Oct 2018BarnesLab/Patient2Vec

The wide implementation of electronic health record (EHR) systems facilitates the collection of large-scale health data from real clinical settings.

FEATURE IMPORTANCE