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 1141–1150 of 1706 papers

TitleStatusHype
Improving SAT Solver Heuristics with Graph Networks and Reinforcement Learning—0
Improving Sequence to Sequence Learning for Morphological Inflection Generation: The BIU-MIT Systems for the SIGMORPHON 2016 Shared Task for Morphological Reinflection—0
Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting—0
Improving VTE Identification through Language Models from Radiology Reports: A Comparative Study of Mamba, Phi-3 Mini, and BERT—0
Improving Warped Planar Object Detection Network For Automatic License Plate Recognition—0
Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition—0
Incremental Parsing with Minimal Features Using Bi-Directional LSTM—0
Incremental Recurrent Neural Network Dependency Parser with Search-based Discriminative Training—0
`Indicatements' that character language models learn English morpho-syntactic units and regularities—0
Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified