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 226–250 of 1706 papers

TitleStatusHype
CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record EmbeddingsCode0
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction—0
360Brew: A Decoder-only Foundation Model for Personalized Ranking and Recommendation—0
Sample-Efficient Behavior Cloning Using General Domain Knowledge—0
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction—0
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
Risk Analysis of Flowlines in the Oil and Gas Sector: A GIS and Machine Learning ApproachCode0
DLinear-based Prediction of Remaining Useful Life of Lithium-Ion Batteries: Feature Engineering through Explainable Artificial Intelligence—0
Algorithmic Derivation of Human Spatial Navigation Indices From Eye Movement Data—0
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Dataset-Agnostic Recommender Systems—0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Predicting Vulnerability to Malware Using Machine Learning Models: A Study on Microsoft Windows Machines—0
Multi-Modal Video Feature Extraction for Popularity Prediction—0
Classification of Operational Records in Aviation Using Deep Learning Approaches—0
Dynamic Adaptation in Data Storage: Real-Time Machine Learning for Enhanced Prefetching—0
Assets Forecasting with Feature Engineering and Transformation Methods for LightGBM—0
Three-Class Text Sentiment Analysis Based on LSTM—0
STAHGNet: Modeling Hybrid-grained Heterogenous Dependency Efficiently for Traffic Prediction—0
Intelligent Approaches to Predictive Analytics in Occupational Health and Safety in India—0
Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading—0
PCA-Featured Transformer for Jamming Detection in 5G UAV Networks—0
Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal—0
GLARE: Google Apps Arabic Reviews DatasetCode0
S&P 500 Trend Prediction—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified