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 1–25 of 1706 papers

TitleStatusHype
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction—0
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey—0
Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection—0
Quantum Reinforcement Learning Trading Agent for Sector Rotation in the Taiwan Stock Market—0
A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners—0
Tabular Feature Discovery With Reasoning Type Exploration—0
Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures—0
Enhancing Forecasting Accuracy in Dynamic Environments via PELT-Driven Drift Detection and Model Adaptation—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
Feature Engineering for Agents: An Adaptive Cognitive Architecture for Interpretable ML Monitoring—0
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition—0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
Next-Generation Conflict Forecasting: Unleashing Predictive Patterns through Spatiotemporal Learning—0
Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data—0
Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer—0
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure ModesCode0
Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks—0
CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical DataCode0
Transforming Podcast Preview Generation: From Expert Models to LLM-Based Systems—0
Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical ImagingCode0
Machine Learning Algorithm for Noise Reduction and Disease-Causing Gene Feature Extraction in Gene Sequencing Data—0
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science—0
Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories—0
Show:102550
← PrevPage 1 of 69Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified