Non-linear shrinking of linear model errors.

Area of Science:

• Data Science
• Machine Learning
• Spectroscopy

Background:

• Artificial neural networks (ANNs) offer powerful predictive capabilities for spectroscopic data analysis.
• However, the 'black box' nature of ANNs hinders model interpretability.
• Combining linear methods with ANNs can address this challenge.

Purpose of the Study:

• To explore residual modeling using modern neural network architectures for high-dimensional spectroscopic data.
• To develop a method that balances predictive performance with model interpretability.
• To advance explainable AI in data modeling.

Main Methods:

• A hybrid approach combining linear modeling with ANNs to model residuals.
• Application of the residual modeling scheme to high-dimensional datasets.
• Development of novel extensions for classification tasks.

Main Results:

• The residual modeling approach achieved strong predictive performance on regression and classification tasks.
• Interpretations from the linear model component were successfully retained.
• Performance comparable to pure ANN models was achieved while enhancing transparency.

Conclusions:

• Residual modeling with ANNs offers a viable path to interpretable machine learning for spectroscopic data.
• This method improves upon linear models by capturing non-linearities.
• The study contributes to making artificial intelligence models more transparent and explainable.