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 541–550 of 1706 papers

TitleStatusHype
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code—0
Multi-Scale Control Signal-Aware Transformer for Motion Synthesis without Phase—0
IoT Device Identification Based on Network Communication Analysis Using Deep Learning—0
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches—0
Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product ReviewsCode0
Streamlining models with explanations in the learning loopCode0
Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars—0
Text2shape Deep Retrieval Model: Generating Initial Cases for Mechanical Part Redesign under the Context of Case-Based Reasoning—0
Scientific Computing with Diffractive Optical Neural Networks—0
A Survey on Arabic Named Entity Recognition: Past, Recent Advances, and Future Trends—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified