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 201–210 of 1706 papers

TitleStatusHype
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Integrating convolutional layers and biformer network with forward-forward and backpropagation trainingCode0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
Mitigating Attrition: Data-Driven Approach Using Machine Learning and Data Engineering—0
Edge Training and Inference with Analog ReRAM Technology for Hand Gesture Recognition—0
TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration—0
ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
Feature Engineering Approach to Building Load Prediction: A Case Study for Commercial Building Chiller Plant Optimization in Tropical Weather—0
EssayJudge: A Multi-Granular Benchmark for Assessing Automated Essay Scoring Capabilities of Multimodal Large Language Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified