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Feature Engineering

Feature engineering is the process of taking a dataset and constructing explanatory variables — features — that can be used to train a machine learning model for a prediction problem. Often, data is spread across multiple tables and must be gathered into a single table with rows containing the observations and features in the columns.

The traditional approach to feature engineering is to build features one at a time using domain knowledge, a tedious, time-consuming, and error-prone process known as manual feature engineering. The code for manual feature engineering is problem-dependent and must be re-written for each new dataset.

Papers

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Showing 211–220 of 1706 papers

TitleStatusHype
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
RelBench: A Benchmark for Deep Learning on Relational DatabasesCode3
Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review—0
An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation—0
Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design—0
Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques—0
Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks—0
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy TranscriptsCode0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph ClassificationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified