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 1251–1260 of 1706 papers

TitleStatusHype
Linguistic Structured Sparsity in Text Categorization—0
LLbezpeky: Leveraging Large Language Models for Vulnerability Detection—0
LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search—0
LLMpatronous: Harnessing the Power of LLMs For Vulnerability Detection—0
LocalGLMnet: interpretable deep learning for tabular data—0
Locally Non-Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization—0
LOLgorithm: Integrating Semantic,Syntactic and Contextual Elements for Humor Classification—0
Long Short-Term Memory Neural Networks for Chinese Word Segmentation—0
Low-Dimensional Discriminative Reranking—0
Low Dimensional State Representation Learning with Reward-shaped Priors—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified