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 491–500 of 1706 papers

TitleStatusHype
Deep Feature Learning for Wireless Spectrum Data—0
AutoML-GPT: Large Language Model for AutoML—0
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
Deep Hashing: A Joint Approach for Image Signature Learning—0
Deep Health Care Text Classification—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
Advanced fraud detection using machine learning models: enhancing financial transaction security—0
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction—0
A Cognition Based Attention Model for Sentiment Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified