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 901–910 of 1706 papers

TitleStatusHype
Online Compact Convexified Factorization Machine—0
Online Conversation Disentanglement with Pointer Networks—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
On the combination of graph data for assessing thin-file borrowers' creditworthiness—0
On the effectiveness of feature set augmentation using clusters of word embeddings—0
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning—0
On the Relevance of Syntactic and Discourse Features for Author Profiling and Identification—0
On the Replicability and Reproducibility of Deep Learning in Software Engineering—0
On the Vulnerability of CNN Classifiers in EEG-Based BCIs—0
Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified