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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 221–230 of 1706 papers

TitleStatusHype
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects—0
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading—0
Asset price movement prediction using empirical mode decomposition and Gaussian mixture models—0
Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification—0
A State-of-the-Art Mention-Pair Model for Coreference Resolution—0
A streamable large-scale clinical EEG dataset for Deep Learning—0
A strong baseline for question relevancy ranking—0
Approaches to Fraud Detection on Credit Card Transactions Using Artificial Intelligence Methods—0
Show:102550
← PrevPage 23 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified