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 921–930 of 1706 papers

TitleStatusHype
PCA-Featured Transformer for Jamming Detection in 5G UAV Networks—0
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models—0
Performing Highly Accurate Predictions Through Convolutional Networks for Actual Telecommunication Challenges—0
Personality Trait Identification Using the Russian Feature Extraction Toolkit—0
Personalized human mobility prediction for HuMob challenge—0
Personalized Web Search—0
p-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning—0
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry—0
Phonologically Aware Neural Model for Named Entity Recognition in Low Resource Transfer Settings—0
Physical Activity Recognition Based on a Parallel Approach for an Ensemble of Machine Learning and Deep Learning Classifiers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified