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An implementation of hyperparameter optimization for Gradient Boosted Trees on binary classification and regression problems. The current version supports two optimization methods: Bayesian optimization and random search. Instead of returning the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection.
# Load German credit data
data(german_credit)
train <- german_credit$train
test <- german_credit$test
target_idx <- german_credit$target_idx
pred_idx <- german_credit$pred_idx
# Train a GBT model with optimization on AUC
model <- gbts(train[, pred_idx], train[, target_idx], nitr = 200, pfmc = "auc")
# Predict on test data
yhat_test <- predict(model, test[, pred_idx])
# Compute AUC on test data
comperf(test[, target_idx], yhat_test, pfmc = "auc")# Load Boston housing data
data(boston_housing)
train <- boston_housing$train
test <- boston_housing$test
target_idx <- boston_housing$target_idx
pred_idx <- boston_housing$pred_idx
# Train a GBT model with optimization on MSE
model <- gbts(train[, pred_idx], train[, target_idx], nitr = 200, pfmc = "mse")
# Predict on test data
yhat_test <- predict(model, test[, pred_idx])
# Compute MSE on test data
comperf(test[, target_idx], yhat_test, pfmc = "mse")To get the current released version from CRAN:
install.packages("gbts")To see a list of functions and datasets provided by gbts:
help(package = "gbts")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.