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Employs decision trees that can handle multiclass splits at each node, often using measures like Gini impurity or entropy for the split criteria. Learning routines grow the tree by choosing the best feature that maximally separates the classes at each node.

Creates a code book where each class is represented by a unique binary string. Multiclass problems are decomposed into multiple binary problems according to these codes. Learning involves associating each class with a unique binary code and training a binary classifier for each bit position.

Involves algorithms like AdaBoost or Gradient Boosting that combine weak learners sequentially, where each subsequent learner attempts to correct the errors of its predecessor. Learning is typically focused on minimizing a loss function that penalizes misclassification across all classes.

Applies SVMs, originally binary classifiers, using strategies like OvO and OvR to manage multiclass datasets. Alternative kernel methods may also be introduced for better class separation. Learning involves optimizing the margin between classes in the high-dimensional feature space.

Involves splitting the multiclass dataset into multiple binary classification problems. A separate model is trained on each problem where one class is treated as the 'class of interest' and all other classes are considered the second class. Learning involves fitting one classifier per class.

Leverages an ensemble of decision trees, predicting the class by majority voting or averaging across all trees in the forest. Learning involves building multiple trees with random subsets of features and samples from the dataset, aiming at reducing overfitting and variance.

Involves splitting the problem into one binary classification problem per pair of classes. A classifier is trained on each pair of classes and learning involves fitting 'n * (n - 1) / 2' classifiers for 'n' classes, where each one determines the superiority of a class over the other.