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 351–375 of 1706 papers

TitleStatusHype
NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading—0
Feature Interaction Aware Automated Data Representation TransformationCode0
Context-Based Tweet Engagement PredictionCode0
Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension—0
SEPT: Towards Efficient Scene Representation Learning for Motion Prediction—0
Early Churn Prediction from Large Scale User-Product Interaction Time Series—0
Prediction Model For Wordle Game Results With High Robustness—0
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation ScoringCode1
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization—0
Baichuan 2: Open Large-scale Language ModelsCode4
SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition SystemsCode0
Leveraging Contextual Information for Effective Entity Salience Detection—0
Feature Engineering in Learning-to-Rank for Community Question Answering Task—0
Native Language Identification with Big Bird EmbeddingsCode0
Rubric-Specific Approach to Automated Essay Scoring with Augmentation Training—0
TFBEST: Dual-Aspect Transformer with Learnable Positional Encoding for Failure Prediction—0
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence—0
Effective Illicit Account Detection on Large Cryptocurrency MultiGraphsCode0
AutoML-GPT: Large Language Model for AutoML—0
What can we learn from quantum convolutional neural networks?—0
Interpolation of mountain weather forecasts by machine learningCode0
SieveNet: Selecting Point-Based Features for Mesh Networks—0
TrajPy: empowering feature engineering for trajectory analysis across domainsCode0
Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering—0
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified