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 531–540 of 1706 papers

TitleStatusHype
Feature Engineering Methods on Multivariate Time-Series Data for Financial Data Science Competitions—0
Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
Clustering US Counties to Find Patterns Related to the COVID-19 Pandemic—0
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects—0
Learning From High-Dimensional Cyber-Physical Data Streams for Diagnosing Faults in Smart Grids—0
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters—0
Deep Learning for Iris Recognition: A Review—0
Deep incremental learning models for financial temporal tabular datasets with distribution shifts—0
Transferable Deep Learning Power System Short-Term Voltage Stability Assessment with Physics-Informed Topological Feature Engineering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified