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 401–410 of 1706 papers

TitleStatusHype
Towards Versatile Graph Learning Approach: from the Perspective of Large Language Models—0
AdvanceSplice: Integrating N-gram one-hot encoding and ensemble modeling for enhanced accuracyCode0
From Data to Decisions: The Transformational Power of Machine Learning in Business Recommendations—0
Descriptive Kernel Convolution Network with Improved Random Walk KernelCode0
Explainable Adversarial Learning Framework on Physical Layer Secret Keys Combating Malicious Reconfigurable Intelligent Surface—0
Unraveling the Key of Machine Learning Solutions for Android Malware Detection—0
Data organization limits the predictability of binary classification—0
GeoDecoder: Empowering Multimodal Map Understanding—0
A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data—0
Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic RegressionCode0
Show:102550
← PrevPage 41 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified