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 131–140 of 1706 papers

TitleStatusHype
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information—0
Federated Automated Feature Engineering—0
Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective—0
Comparative Performance of Machine Learning Algorithms for Early Genetic Disorder and Subclass Classification—0
Intelligent Spark Agents: A Modular LangGraph Framework for Scalable, Visualized, and Enhanced Big Data Machine Learning Workflows—0
HiCat: A Semi-Supervised Approach for Cell Type Annotation—0
An AutoML-based approach for Network Intrusion Detection—0
Enhancing Molecular Design through Graph-based Topological Reinforcement Learning—0
Understanding LLM Embeddings for Regression—0
Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified