Machine Learning Model Performance on Plasmonic Sensor Metrics

Figshare hosts a 5.5 KB dataset analyzing three machine learning regression models. The data compares model performance on confinement loss and wavelength sensitivity metrics for enhanced plasmonic sensors. Author Sonia Akter contributed this analysis under a CC BY 4.0 license.

Use Cases

• Compare Random forest regression performance against other models for predicting confinement loss.
• Analyze algorithm computational framework results on wavelength sensitivity metrics.
• Benchmark model accuracy for predicting optical responses in surface plasmon resonance sensor designs.

Strengths

• Dataset is openly licensed under CC BY 4.0.
• Focus includes two critical performance metrics: confinement loss and wavelength sensitivity.
• Analysis covers three distinct machine learning regression models.

Limitations

• Dataset is extremely small at 5.5 KB, offering limited scope for model training or validation.
• No column definitions or sample data are provided, hindering immediate analysis.
• The dataset's origin and methodological details for data gathering are unspecified.

Provenance

Source

figshare

Collection Method

Performance analysis of machine learning models; specific data collection method unknown.

Time Range

null

Freshness

Last updated March 2026.

Geography

null