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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 31–40 of 1706 papers

TitleStatusHype
DeltaPy: A Framework for Tabular Data Augmentation in PythonCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Can Models Help Us Create Better Models? Evaluating LLMs as Data ScientistsCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using PhotoplethysmogramCode1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring SystemsCode1
Cardea: An Open Automated Machine Learning Framework for Electronic Health RecordsCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified