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 1241–1250 of 1706 papers

TitleStatusHype
Leveraging Machine Learning for Early Autism Detection via INDT-ASD Indian Database—0
Leveraging Open-Source Large Language Models for Native Language Identification—0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Leveraging sinusoidal representation networks to predict fMRI signals from EEG—0
Lexical Bias In Essay Level Prediction—0
LFG-based Features for Noun Number and Article Grammatical Errors—0
LiDAR-based Outdoor Crowd Management for Smart Campus on the Edge—0
LightGBM robust optimization algorithm based on topological data analysis—0
LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification—0
Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecasting—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified