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 561–570 of 1706 papers

TitleStatusHype
A Hybrid Model for Forecasting Short-Term Electricity Demand—0
Learning latent representations for operational nitrogen response rate prediction—0
A Survey on Semantics in Automated Data Science—0
On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning—0
TaDeR: A New Task Dependency Recommendation for Project Management Platform—0
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategyCode1
A simple framework for contrastive learning phases of matter—0
Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor NetworksCode0
On Designing Data Models for Energy Feature Stores—0
SeqNet: An Efficient Neural Network for Automatic Malware Detection—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified