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 91–100 of 1706 papers

TitleStatusHype
AutoML: A Survey of the State-of-the-ArtCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud DetectionCode1
A Hybrid Rule-Based and Neural Coreference Resolution System with an Evaluation on Dutch LiteratureCode1
Discovering Neural WiringsCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational DataCode1
Bayesian Optimization of Catalysis With In-Context LearningCode1
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified