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 391–400 of 1706 papers

TitleStatusHype
Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data—0
randomHAR: Improving Ensemble Deep Learners for Human Activity Recognition with Sensor Selection and Reinforcement Learning—0
Benchmarks and Custom Package for Energy ForecastingCode1
Transaction Fraud Detection via an Adaptive Graph Neural Network—0
An Examination of Wearable Sensors and Video Data Capture for Human Exercise Classification—0
A Natural Language Processing Approach to Malware Classification—0
Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization PerspectiveCode0
Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall ClassificationCode0
A Food Recommender System in Academic Environments Based on Machine Learning Models—0
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified