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

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Showing 1361–1370 of 1706 papers

TitleStatusHype
Object-Category Aware Reinforcement Learning—0
OCR Post-Processing Text Correction using Simulated Annealing (OPTeCA)—0
OmniGraph: Rich Representation and Graph Kernel Learning—0
On Designing Data Models for Energy Feature Stores—0
One button machine for automating feature engineering in relational databases—0
One-Shot Imitation Learning—0
OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification—0
Online Compact Convexified Factorization Machine—0
Online Conversation Disentanglement with Pointer Networks—0
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified