An implementation of the AdaBoost algorithm from Freund and Shapire (1997) applied to decision tree classifiers.
adaboost(X, y, tree_depth = 3, n_rounds = 100, verbose = FALSE,
control = NULL)A matrix of continuous predictors.
A vector of responses with entries in c(-1, 1).
The depth of the base tree classifier to use.
The number of rounds of boosting to use.
Whether to print the number of iterations.
A rpart.control list that controls properties of fitted
decision trees.
Returns an object of class adaboost containing the following values:
Weights computed in the adaboost fit.
The trees constructed in each round of boosting. Storing trees allows one to make predictions on new data.
A confusion matrix for the in-sample fits.
Freund, Y. and Schapire, R. (1997). A decision-theoretic generalization of online learning and an application to boosting, Journal of Computer and System Sciences 55: 119-139.
# NOT RUN {
# Generate data from the circle model
set.seed(111)
dat = circle_data(n = 500)
train_index = sample(1:500, 400)
ada = adaboost(dat$X[train_index,], dat$y[train_index], tree_depth = 2,
n_rounds = 200, verbose = TRUE)
print(ada)
yhat_ada = predict(ada, dat$X[-train_index,])
# calculate misclassification rate
mean(dat$y[-train_index] != yhat_ada)
# }
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