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 441–450 of 1706 papers

TitleStatusHype
Leveraging sinusoidal representation networks to predict fMRI signals from EEG—0
CLIP-Motion: Learning Reward Functions for Robotic Actions Using Consecutive Observations—0
RELand: Risk Estimation of Landmines via Interpretable Invariant Risk MinimizationCode0
ReConTab: Regularized Contrastive Representation Learning for Tabular Data—0
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting—0
Bi-Encoders based Species Normalization -- Pairwise Sentence Learning to Rank—0
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes—0
Personalized human mobility prediction for HuMob challenge—0
A Framework for Empowering Reinforcement Learning Agents with Causal Analysis: Enhancing Automated Cryptocurrency Trading—0
A Novel Statistical Measure for Out-of-Distribution Detection in Data Quality Assurance—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified