An adaptive Monte Carlo method for evaluating uncertainty in confidence level of non-targeted principal component analysis classification

Key Points

• Results indicate improved confidence levels in classification outcomes through an adaptive Monte Carlo approach.
• The Monte Carlo method showed significant reductions in uncertainty, improving the results of principal component analysis.
• Analysis involves innovative techniques to assess uncertainty and enhance the interpretability of classification models.
• Implications suggest a need for more robust analytical methods for non-targeted classification strategies.