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 461–470 of 1706 papers

TitleStatusHype
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
A Feature Engineering Approach for Literary and Colloquial Tamil Speech Classification using 1D-CNN—0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
Data-Driven Investigative Journalism For Connectas Dataset—0
Data-driven Smart Ponzi Scheme Detection—0
autoNLP: NLP Feature Recommendations for Text Analytics Applications—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
A Data-Centric Behavioral Machine Learning Platform to Reduce Health Inequalities—0
A Comparative Analysis of Android Malware—0
Show:102550
← PrevPage 47 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified