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 521–530 of 1706 papers

TitleStatusHype
Enhancing Sindhi Word Segmentation using Subword Representation Learning and Position-aware Self-attention—0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective—0
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code—0
A Model of Coherence Based on Distributed Sentence Representation—0
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems—0
DAG-based Long Short-Term Memory for Neural Word Segmentation—0
Customer Support Ticket Escalation Prediction using Feature Engineering—0
A strong baseline for question relevancy ranking—0
AMEIR: Automatic Behavior Modeling, Interaction Exploration and MLP Investigation in the Recommender System—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified