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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 581–590 of 1706 papers

TitleStatusHype
Feature Selection with Distance Correlation—0
Deduplication Over Heterogeneous Attribute Types (D-HAT)Code0
Photometric identification of compact galaxies, stars and quasars using multiple neural networksCode0
Small Language Models for Tabular DataCode0
A Comparison of SVM against Pre-trained Language Models (PLMs) for Text Classification Tasks—0
Feature Engineering vs BERT on Twitter Data—0
End-to-end Ensemble-based Feature Selection for Paralinguistics Tasks—0
Automatic Seizure Prediction using CNN and LSTM—0
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified