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 281–290 of 1706 papers

TitleStatusHype
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems—0
Enriching Tabular Data with Contextual LLM Embeddings: A Comprehensive Ablation Study for Ensemble Classifiers—0
Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-SeriesCode0
Machine Learning Framework for Audio-Based Content Evaluation using MFCC, Chroma, Spectral Contrast, and Temporal Feature Engineering—0
Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model—0
Large Language Models Engineer Too Many Simple Features For Tabular DataCode0
AdaptoML-UX: An Adaptive User-centered GUI-based AutoML Toolkit for Non-AI Experts and HCI ResearchersCode0
Molecular Topological Profile (MOLTOP) - Simple and Strong Baseline for Molecular Graph ClassificationCode0
Reproducible Machine Learning-based Voice Pathology Detection: Introducing the Pitch Difference FeatureCode0
Statistical Test for Auto Feature Engineering by Selective InferenceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified