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 1671–1680 of 1706 papers

TitleStatusHype
Binary Classification as a Phase Separation ProcessCode0
Comparing the Effects of Persistence Barcodes Aggregation and Feature Concatenation on Medical ImagingCode0
A Multi-level Neural Network for Implicit Causality Detection in Web TextsCode0
Understanding learning from EEG data: Combining machine learning and feature engineering based on hidden Markov models and mixed modelsCode0
Stock Movement Prediction from Tweets and Historical PricesCode0
CNN-LSTM Hybrid Model for AI-Driven Prediction of COVID-19 Severity from Spike Sequences and Clinical DataCode0
Stop overkilling simple tasks with black-box models and use transparent models insteadCode0
Streamlining models with explanations in the learning loopCode0
CLRGaze: Contrastive Learning of Representations for Eye Movement SignalsCode0
Photometric identification of compact galaxies, stars and quasars using multiple neural networksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified