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 111–120 of 1706 papers

TitleStatusHype
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language ModelCode1
Representation learning of writing styleCode1
Online learning techniques for prediction of temporal tabular datasets with regime changesCode1
Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoderCode1
Short-term Renewable Energy Forecasting in Greece using Prophet Decomposition and Tree-based EnsemblesCode1
Simplified DOM Trees for Transferable Attribute Extraction from the WebCode1
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language ModelCode1
SMUTF: Schema Matching Using Generative Tags and Hybrid FeaturesCode1
Supervised Learning on Relational Databases with Graph Neural NetworksCode1
Deep & Cross Network for Ad Click PredictionsCode1
Show:102550
← PrevPage 12 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified