Pattern classification - Brain Research Imaging Centre Edinburgh

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Accuracy & Dataset balance Watch out if #samples/class are different! Example: Good overall accuracy (72%) but •Majority class (N 1 = 80), excellent accuracy (90%) •Minority class (N 2 = 20), poor accuracy (0%) Good practice: Report •class accuracies [p 0 , p 1 , …, p C ] •balanced accuracy p bal = (p 0 + p 1 + …+ p C )/C
Regression MSE • LOO error in one fold • Across all LOO folds Out-of-sample “mean squared error” (MSE) Other measure: Correlation between predictions (across folds!) and ‘true’ targets
Page 1 and 2: Pattern Recognition for Neuroimagin
Page 3 and 4: Overview • Introduction - Univari
Page 5 and 6: BOLD signal Univariate vs. multivar
Page 7 and 8: Neuroimaging data “feature vector
Page 9 and 10: Pattern recognition framework Input
Page 11 and 12: voxel 2 Classification example L R
Page 13 and 14: Kernel approaches • Mathematical
Page 15 and 16: Linear classifier • hyperplanes t
Page 17 and 18: Support Vector Machine SVM = “max
Page 19 and 20: SVM vs. GP SVM Hard binary classif
Page 21 and 22: Training set Test set … … …
Page 23: Confusion matrix & accuracy Confusi
Page 29 and 30: Example of masks Linear machine Wei
Page 33 and 34: Design Between subjects •2 groups
Page 35 and 36: Design: Within subject • Block or
Page 37 and 38: Design: Within subject • Block or
Page 39 and 40: Multiclass problem Multiclass machi
Page 41: Conclusions Key points: • More se
Page 44: Thank you for your attention! Any q

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