How deeply does Ensemble Methods for Machine Learning cover multiclass classification?

Defines the task and demonstrates library-based ensemble use, while deferring general multiclass algorithms and detailed probability mechanics.

Multiclass classification

As explained in Ensemble Methods for Machine Learning

Multiclass classification is a supervised prediction task whose target has more than two categorical classes.

Multiclass classification predicts one categorical label from more than two possible classes. It extends binary classification beyond yes-or-no decisions, such as assigning a query-document pair to least, moderate, or high relevance. section 1.3 section 6.5.1

The task appears in several modeling settings:

Predict a relevance class for each query-document pair in the LETOR data set. section 6.5.1
Use an AdaBoost ensemble whose implementation supports more than two target classes. section 4.2.3
Represent each class probability separately when combining predictions from multiple classifiers. section 3.1.2

AdaBoost on Iris

A scikit-learn example loads the Iris data, splits it into training and test sets, trains 20 shallow-tree AdaBoost learners with learning_rate=0.75 and algorithm='SAMME.R', and reports a test error of 0.07895. section 4.2.3

ensemble = AdaBoostClassifier(base_estimator=shallow_tree,
                             n_estimators=20,              
                             learning_rate=0.75, algorithm='SAMME.R')
ensemble.fit(Xtrn, ytrn)
ypred = ensemble.predict(Xtst)
err = 1 - accuracy_score(ytst, ypred)
print(err)

Shows the complete multiclass AdaBoost training, prediction, and accuracy-error calculation. section 4.2.3

How Ensemble Methods for Machine Learning covers it

Cover of Ensemble Methods for Machine Learning

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Deep Learning with R, Third EditionA multiclass classification model is a predictive model that assigns inputs to one of more than two classes and produces scores or probabilities for those classes.

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Related concepts

classifierkind of · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
random forestused by · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
cross entropy lossused by · Cross-entropy loss measures the expected negative log probability that a predicted distribution assigns to outcomes drawn from a target distribution.
multilabel classificationincludes · Multilabel classification is a classification task in which each input may be assigned zero or more labels simultaneously.

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