SOTAVerified

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 701–710 of 1706 papers

TitleStatusHype
A Physics-Informed Machine Learning Approach for Solving Heat Transfer Equation in Advanced Manufacturing and Engineering Applications—0
Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews—0
A Decade Survey of Content Based Image Retrieval using Deep Learning—0
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package—0
A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data—0
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
Explainable Automatic Grading with Neural Additive Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified