Figure 8. The super learner coefficient versus the number of voxels the algorithm is fit on for the (A) unnormalized and the (B) smoothed and moments feature vectors.

As the number of voxels used to fit the algorithm changes, the super learner consistently assigns large weights to the same small number of algorithms. For the unnormalized feature vector, high coefficient weights are selected for the logistic regression, one of the random forest tuning parameters, and the Gaussian mixture model. On the smoothed and moments feature vector, the super learner favors the less complex algorithms: logistic regression, the quadratic discriminant analysis, and the linear discriminant analysis. Some weight is also assigned to the Gaussian mixture model and the random forest.