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 671–680 of 1706 papers

TitleStatusHype
Precog-LTRC-IIITH at GermEval 2021: Ensembling Pre-Trained Language Models with Feature EngineeringCode0
Growing Cosine Unit: A Novel Oscillatory Activation Function That Can Speedup Training and Reduce Parameters in Convolutional Neural Networks—0
Time Series Prediction using Deep Learning Methods in Healthcare—0
End-To-End Anomaly Detection for Identifying Malicious Cyber Behavior through NLP-Based Log Embeddings—0
PTRAIL -- A python package for parallel trajectory data preprocessingCode1
Towards Personalized and Human-in-the-Loop Document Summarization—0
Data-driven Smart Ponzi Scheme Detection—0
Graph Contrastive Learning for Anomaly DetectionCode1
Feature Engineering with Regularity StructuresCode0
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified