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 481–490 of 1706 papers

TitleStatusHype
Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models—0
Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based ModelsCode0
Sparse Array Design for Direction Finding using Deep Learning—0
Deep Feature Learning for Wireless Spectrum Data—0
Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion—0
Multi-output Headed Ensembles for Product Item Classification—0
Adversarial training for tabular data with attack propagation—0
End-to-End Deep Transfer Learning for Calibration-free Motor Imagery Brain Computer Interfaces—0
Alzheimer's Disease Detection from Spontaneous Speech and Text: A review—0
Predicting Battery Lifetime Under Varying Usage Conditions from Early Aging Data—0
Show:102550
← PrevPage 49 of 171Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified