the best value for the regularization parameter C.

the training process of a linear SVM.

data by mapping it to a higher-dimensional space.

that are misclassified by the hyperplane.

farthest from the decision boundary.

closest to the decision boundary (hyperplane).

subset of features when splitting nodes in each tree.

weights to the final predictions.

random subset of training samples for each tree.

possible margin between the classes.

vectors.

the distance between data points.

data.

in parallel on different random subsets of the training data.

weights to different models based on their individual performance.

one correcting the errors of the previous one.

algorithm.

mean of all the data points belonging to a cluster.

does not belong to any cluster.

single data point on the edge of a cluster.

harmonic mean of precision and recall, providing a balance between them.

accuracy and precision.

the data.

Dimensionality