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 326–350 of 1706 papers

TitleStatusHype
Augmenting train maintenance technicians with automated incident diagnostic suggestions—0
EEG Right & Left Voluntary Hand Movement-based Virtual Brain-Computer Interfacing Keyboard Using Hybrid Deep Learning Approach—0
Detecting Unsuccessful Students in Cybersecurity Exercises in Two Different Learning EnvironmentsCode0
Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT—0
LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification—0
Focal Depth Estimation: A Calibration-Free, Subject- and Daytime Invariant Approach—0
IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model—0
Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability—0
AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language ModelsCode0
Stochastic Parrots or ICU Experts? Large Language Models in Critical Care Medicine: A Scoping Review—0
An Efficient and Flexible Deep Learning Method for Signal Delineation via Keypoints Estimation—0
Self-Reasoning Assistant Learning for non-Abelian Gauge Fields Design—0
Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques—0
Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy TranscriptsCode0
Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks—0
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs—0
Molecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph ClassificationCode0
GPT Assisted Annotation of Rhetorical and Linguistic Features for Interpretable Propaganda Technique Detection in News Text—0
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs—0
MERGE -- A Bimodal Audio-Lyrics Dataset for Static Music Emotion Recognition—0
Advancing Automated Deception Detection: A Multimodal Approach to Feature Extraction and AnalysisCode0
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques—0
GraphCNNpred: A stock market indices prediction using a Graph based deep learning system—0
OSPC: Artificial VLM Features for Hateful Meme Detection—0
PharmaGPT: Domain-Specific Large Language Models for Bio-Pharmaceutical and Chemistry—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified