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 181–190 of 1706 papers

TitleStatusHype
HMPE:HeatMap Embedding for Efficient Transformer-Based Small Object Detection—0
Morphing-based Compression for Data-centric ML Pipelines—0
Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes—0
Bringing Structure to Naturalness: On the Naturalness of ASTs—0
Boosting Relational Deep Learning with Pretrained Tabular ModelsCode0
Feature Engineering on LMS Data to Optimize Student Performance Prediction—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science—0
FeRG-LLM : Feature Engineering by Reason Generation Large Language Models—0
Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified