Hyperparameter Search for Gradient Boosted Trees.

Package: gbts

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.

Example

Binary Classification

# 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")

Regression

# 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")

Installation

To get the current released version from CRAN:

install.packages("gbts")

Main Components

To see a list of functions and datasets provided by gbts:

help(package = "gbts")