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 311–320 of 1706 papers

TitleStatusHype
Benchmark Performance of Machine And Deep Learning Based Methodologies for Urdu Text Document Classification—0
A Flexible and Easy-to-use Semantic Role Labeling Framework for Different Languages—0
Capturing ``attrition intensifying'' structural traits from didactic interaction sequences of MOOC learners—0
Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide—0
Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications—0
Affect inTweets: A Transfer Learning Approach—0
Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs—0
Behavioral Modeling for Churn Prediction: Early Indicators and Accurate Predictors of Custom Defection and Loyalty—0
A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection—0
A Feature Induction Algorithm with Application to Named Entity Disambiguation—0
Show:102550
← PrevPage 32 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified