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 421–430 of 1706 papers

TitleStatusHype
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network—0
Ensemble Learning to Assess Dynamics of Affective Experience Ratings and Physiological Change—0
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting—0
Noninvasive Estimation of Mean Pulmonary Artery Pressure Using MRI, Computer Models, and Machine Learning—0
USTHB at NADI 2023 shared task: Exploring Preprocessing and Feature Engineering Strategies for Arabic Dialect Identification—0
Morphological Profiling for Drug Discovery in the Era of Deep Learning—0
Keyword spotting -- Detecting commands in speech using deep learning—0
Unsupervised Multi-modal Feature Alignment for Time Series Representation Learning—0
GFS: Graph-based Feature Synthesis for Prediction over Relational Databases—0
Graph Coordinates and Conventional Neural Networks -- An Alternative for Graph Neural NetworksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified