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 921–930 of 1706 papers

TitleStatusHype
Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering—0
Efficient Deep Feature Calibration for Cross-Modal Joint Embedding Learning—0
Efficient or Powerful? Trade-offs Between Machine Learning and Deep Learning for Mental Illness Detection on Social Media—0
Effort Estimation in Named Entity Tagging Tasks—0
Effort-free Automated Skeletal Abnormality Detection of Rat Fetuses on Whole-body Micro-CT Scans—0
EICA at SemEval-2017 Task 4: A Simple Convolutional Neural Network for Topic-based Sentiment Classification—0
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering—0
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG—0
Elephant: Sequence Labeling for Word and Sentence Segmentation—0
Embedding Domain-Specific Knowledge from LLMs into the Feature Engineering Pipeline—0
Show:102550
← PrevPage 93 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified