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 551–560 of 1706 papers

TitleStatusHype
Stop overkilling simple tasks with black-box models and use transparent models insteadCode0
Mnemosyne: Learning to Train Transformers with Transformers—0
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection—0
Learning a Data-Driven Policy Network for Pre-Training Automated Feature Engineering—0
An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation—0
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation PoliciesCode0
Data-driven intelligent computational design for products: Method, techniques, and applications—0
G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P NetworkCode0
Machine Learning Methods for Cancer Classification Using Gene Expression Data: A ReviewCode0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified