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 51–75 of 1706 papers

TitleStatusHype
AutoGL: A Library for Automated Graph LearningCode1
FASER: Binary Code Similarity Search through the use of Intermediate RepresentationsCode1
DeepFM: A Factorization-Machine based Neural Network for CTR PredictionCode1
Deep & Cross Network for Ad Click PredictionsCode1
AutoML: A Survey of the State-of-the-ArtCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
General-Purpose User Embeddings based on Mobile App UsageCode1
Generative Pre-Training from MoleculesCode1
CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code MatchingCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Deep Dive into Hunting for LotLs Using Machine Learning and Feature Engineering.Code1
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Can Models Help Us Create Better Models? Evaluating LLMs as Data ScientistsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
CASPR: Customer Activity Sequence-based Prediction and RepresentationCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate PredictionCode1
Anomaly Detection for Solder Joints Using β-VAECode1
Context-Aware Deep Learning for Multi Modal Depression DetectionCode1
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified