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 721–730 of 1706 papers

TitleStatusHype
Learning post-processing for QRS detection using Recurrent Neural Network—0
Post-hoc Models for Performance Estimation of Machine Learning Inference—0
GenTAL: Generative Denoising Skip-gram Transformer for Unsupervised Binary Code Similarity Detection—0
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor—0
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?—0
SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver—0
Unsupervised Continual Learning in Streaming Environments—0
Feature Engineering for US State Legislative Hearings: Stance, Affiliation, Engagement and Absentees—0
Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification—0
Beyond Glass-Box Features: Uncertainty Quantification Enhanced Quality Estimation for Neural Machine Translation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified