Description Usage Arguments Value Examples
This function estimates parameters for Random Forest based on bayesian optimization.
1 2 3 4 5 |
train_data |
A data frame for training of Random Forest |
train_label |
The column of class to classify in the training data |
test_data |
A data frame for training of xgboost |
test_label |
The column of class to classify in the test data |
num_tree |
The range of the number of trees for forest. Defaults to 500 (no optimization). |
mtry_range |
Value of mtry used. Defaults from 1 to number of features. |
min_node_size_range |
The range of minimum node sizes to best tested. Default min is 1 and max is sqrt(nrow(train_data)). |
init_points |
Number of randomly chosen points to sample the target function before Bayesian Optimization fitting the Gaussian Process. |
n_iter |
Total number of times the Bayesian Optimization is to repeated. |
acq |
Acquisition function type to be used. Can be "ucb", "ei" or "poi".
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kappa |
tunable parameter kappa of GP Upper Confidence Bound, to balance exploitation against exploration, increasing kappa will make the optimized hyperparameters pursuing exploration. |
eps |
tunable parameter epsilon of Expected Improvement and Probability of Improvement, to balance exploitation against exploration, increasing epsilon will make the optimized hyperparameters are more spread out across the whole range. |
optkernel |
Kernel (aka correlation function) for the underlying Gaussian Process. This parameter should be a list that specifies the type of correlation function along with the smoothness parameter. Popular choices are square exponential (default) or matern 5/2 |
The test accuracy and a list of Bayesian Optimization result is returned:
Best_Par
a named vector of the best hyperparameter set found
Best_Value
the value of metrics achieved by the best hyperparameter set
History
a data.table
of the bayesian optimization history
Pred
a data.table
with validation/cross-validation prediction for each round of bayesian optimization history
1 2 3 4 5 6 7 8 9 10 11 | library(MlBayesOpt)
set.seed(71)
res0 <- rf_opt(train_data = iris_train,
train_label = Species,
test_data = iris_test,
test_label = Species,
mtry_range = c(1L, ncol(iris_train) - 1),
num_tree = 10L,
init_points = 10,
n_iter = 1)
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