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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 71–80 of 1706 papers

TitleStatusHype
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
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
Can Models Help Us Create Better Models? Evaluating LLMs as Data ScientistsCode1
CASPR: Customer Activity Sequence-based Prediction and RepresentationCode1
CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTCode1
A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular DataCode1
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global InferenceCode1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified