Evaluation of machine learning algorithms and structural features for optimal MRI-based diagnostic prediction in psychosis
Fig 5
Classification accuracies for each combination of algorithm and feature type applied to the bipolar disorder vs. schizophrenia classification.
Mean accuracy for the 10 test samples (in green), approximate 95% confidence interval for the mean accuracy (in blue) and highest and lowest accuracy values (in red) are shown for each combination. Rid: Ridge regression, Las: Lasso regression, Ela: Elastic net regularization, L0: L0-norm regularization, SVC: Support vector classifier, RDA: Regularized discriminant analysis, GPC: Gaussian process classifier, RF: Random forest.