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 701–710 of 1706 papers

TitleStatusHype
Explainable Automatic Grading with Neural Additive Models—0
Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models—0
Explainable Multi-class Classification of Medical Data—0
Explainable Neural Networks based on Additive Index Models—0
Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector—0
Explaining Classifiers Trained on Raw Hierarchical Multiple-Instance Data—0
Explaining Translationese: why are Neural Classifiers Better and what do they Learn?—0
Exploiting Meta-Cognitive Features for a Machine-Learning-Based One-Shot Group-Decision Aggregation—0
Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish—0
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified