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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1301–1310 of 1706 papers

TitleStatusHype
Modelling customer churn for the retail industry in a deep learning based sequential framework—0
Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network—0
Morphing-based Compression for Data-centric ML Pipelines—0
Morphological Disambiguation from Stemming Data—0
Morphological Profiling for Drug Discovery in the Era of Deep Learning—0
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks—0
MPEC: Manifold-Preserved EEG Classification via an Ensemble of Clustering-Based Classifiers—0
MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction—0
Multi-Layer Perceptron Neural Network for Improving Detection Performance of Malicious Phishing URLs Without Affecting Other Attack Types Classification—0
Multi-level Translation Quality Prediction with QuEst++—0
Show:102550
← PrevPage 131 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified