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 901–910 of 1706 papers

TitleStatusHype
On the Replicability and Reproducibility of Deep Learning in Software Engineering—0
AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced LossCode0
Graph Convolutional Neural Networks for analysis of EEG signals, BCI applicationCode0
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
Semantic Loss Application to Entity Relation Recognition—0
DeepVar: An End-to-End Deep Learning Approach for Genomic Variant Recognition in Biomedical Literature—0
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification—0
Deep Learning for Insider Threat Detection: Review, Challenges and Opportunities—0
Unlocking New York City Crime Insights using Relational Database Embeddings—0
Show:102550
← PrevPage 91 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified