Figure 1

Estimation of the performance of classification of breast cancer samples. A – The total error rate was plotted against the number of features (i.e. spectral components) in the classifier. Shown are average error rates and 95% confidence intervals calculated based on 1000 random validation experiments with 50:50 training/validation split of data. B – Estimation of the sensitivity and specificity of the classification for classifiers built of three or four spectral components. The ROC curve was computed by changing the value of the probability threshold in the SVM classifier from 0.0 to 1.0, and averaging the specificity obtained versus sensitivity rate over 1000 random repeats of training and validation.