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 411–420 of 1706 papers

TitleStatusHype
Design & Implementation of Automatic Machine Condition Monitoring and Maintenance System in Limited Resource Situations—0
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores—0
Novel Representation Learning Technique using Graphs for Performance Analytics—0
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness—0
Few-Shot Learning for Chronic Disease Management: Leveraging Large Language Models and Multi-Prompt Engineering with Medical Knowledge Injection—0
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection—0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
LLbezpeky: Leveraging Large Language Models for Vulnerability Detection—0
Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock—0
TSPP: A Unified Benchmarking Tool for Time-series ForecastingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CNN14 gestures accuracy0.98—Unverified