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 301–310 of 1706 papers

TitleStatusHype
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion DetectionCode0
Reinforcement Feature Transformation for Polymer Property Performance Prediction—0
A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN—0
Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks—0
Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety—0
Leveraging Open-Source Large Language Models for Native Language Identification—0
Learn2Aggregate: Supervised Generation of Chvátal-Gomory Cuts Using Graph Neural Networks—0
HybridFC: A Hybrid Fact-Checking Approach for Knowledge GraphsCode0
Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration—0
IIFE: Interaction Information Based Automated Feature EngineeringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified