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 741–750 of 1706 papers

TitleStatusHype
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
Test Automation with Grad-CAM Heatmaps -- A Future Pipe Segment in MLOps for Vision AI?—0
Memory-based Deep Reinforcement Learning for POMDPsCode1
DNN2LR: Automatic Feature Crossing for Credit Scoring—0
Symbolic regression for scientific discovery: an application to wind speed forecastingCode1
Robust PDF Document Conversion Using Recurrent Neural Networks—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
Geometric feature performance under downsampling for EEG classification tasks—0
Deep Learning Based Walking Tasks Classification in Older Adults using fNIRS—0
Feature Engineering for Scalable Application-Level Post-Silicon Debugging—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified