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 1191–1200 of 1706 papers

TitleStatusHype
Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network—0
Language Semantics Interpretation with an Interaction-based Recurrent Neural Networks—0
Large Language Models for Networking: Workflow, Advances and Challenges—0
Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level—0
Large Margin Prototypical Network for Few-shot Relation Classification with Fine-grained Features—0
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models—0
Large-Scale Cell-Level Quality of Service Estimation on 5G Networks Using Machine Learning Techniques—0
Large-scale End-of-Life Prediction of Hard Disks in Distributed Datacenters—0
Latent Variable Session-Based Recommendation—0
Lateral Movement Detection Using User Behavioral Analysis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified