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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 276–300 of 1706 papers

TitleStatusHype
RAGulator: Lightweight Out-of-Context Detectors for Grounded Text Generation—0
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level—0
Correlation of Object Detection Performance with Visual Saliency and Depth EstimationCode0
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models—0
Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems—0
See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers—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
Large Language Models Engineer Too Many Simple Features For Tabular DataCode0
Predicting 30-Day Hospital Readmission in Medicare Patients: Insights from an LSTM Deep Learning Model—0
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
ELF-Gym: Evaluating Large Language Models Generated Features for Tabular PredictionCode0
Statistical Test for Auto Feature Engineering by Selective InferenceCode0
Sui Generis: Large Language Models for Authorship Attribution and Verification in Latin—0
Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model—0
Principal Orthogonal Latent Components Analysis (POLCA Net)Code0
Neural-Bayesian Program Learning for Few-shot Dialogue Intent Parsing—0
Learning to Solve Abstract Reasoning Problems with Neurosymbolic Program Synthesis and Task Generation—0
Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks—0
Semantic-Guided RL for Interpretable Feature Engineering—0
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach—0
Automatic deductive coding in discourse analysis: an application of large language models in learning analyticsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified