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Feature Importance datasets

archive 2025-07-28

6 datasets carry the task tag "Feature Importance" (the task itself: Feature Importance), ordered by the archive's paper count. Page 1 of 1: 6 shown of 6. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

All 12,172 Question Answering 413Semantic Segmentation 347Object Detection 332Image Classification 283Classification 222Text Classification 167Language Modelling 166Text Generation 162Visual Question Answering (VQA) 144Named Entity Recognition (NER) 130Pose Estimation 124Anomaly Detection 119Action Recognition 115Instance Segmentation 111Sentiment Analysis 104Text Summarization 98Domain Adaptation 96Speech Recognition 96Reading Comprehension 95Information Retrieval 932D Object Detection 92Image Retrieval 87Machine Translation 82Relation Extraction 822D Semantic Segmentation 81Natural Language Inference 81Image Captioning 79Image Generation 79Depth Estimation 77Node Classification 75Natural Language Understanding 72Autonomous Driving 70Code Generation 70Object Tracking 693D Object Detection 67Face Recognition 67Link Prediction 67Person Re-Identification 65Data Augmentation 63Multi-Task Learning 593D Reconstruction 58Common Sense Reasoning 56Recommendation Systems 56Video Understanding 56Optical Character Recognition (OCR) 55Graph Classification 54Abstractive Text Summarization 52Emotion Recognition 52Medical Image Segmentation 52Word Embeddings 52Feature Importance 6

Feature Importance datasets 1–6 of 6

Digits (Optical Recognition of Handwritten Digits)
The DIGITS dataset consists of 1797 8×8 grayscale images (1439 for training and 360 for testing) of handwritten digits.
ImagesGraph ClassificationFeature ImportanceHandwritten Digit Recognition
21 papers · 3 benchmarks
iris
The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician, eugenicist, and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an…
DenoisingClustering Algorithms EvaluationGeneral ClassificationImage/Document ClusteringFeature ImportanceQuantum Machine Learning+2 tasks
20 papers · 8 benchmarks
Wine (Wine Data Set)
These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars.
ImagesGraph ClassificationAutoMLGeneral ClassificationImage/Document ClusteringFeature ImportanceIncremental Constrained Clustering
11 papers · 6 benchmarks
Diabetes (Diabetes 130-US Hospitals for Years 1999-2008)
What do the instances in this dataset represent?
TabularFeature ImportanceTabular Data GenerationDiabetes Prediction
5 papers · 3 benchmarks
Hotel (Hospitality > Tourism > Hotel Demand/Sales)
The dataset contains the hotel demand and revenue of 8 major tourist destinations in the US (e.g., Los Angeles, Orlando ...).
TabularTime seriesTime Series AnalysisTime Series ForecastingFeature ImportanceTime Series PredictionProbabilistic Deep Learningfeature selection
2 papers · 0 benchmarks
simply-CLEVR
The simply-CLEVR dataset aims to provide a benchmark dataset that can be used for transparent quantitative evaluation of explanation methods (aka heatmaps/XAI methods).
ImagesTextsQuestion AnsweringVisual Question Answering (VQA)Feature Importance
1 paper · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.