Description Usage Arguments Value Examples
This function estimates parameters for SVM(Gaussian Kernel) based on bayesian optimization
1 2 3 4 5 |
data |
data |
label |
label for classification |
gamma_range |
The range of gamma. Default is c(10 ^ (-3), 10 ^ 1) |
cost_range |
The range of C(Cost). Default is c(10 ^ (-2), 10 ^ 2) |
svm_kernel |
Kernel used in SVM. You might consider changing some of the following parameters, depending on the kernel type.
|
degree_range |
Parameter needed for kernel of type polynomial. Default is c(3L, 10L) |
coef0_range |
Parameter needed for kernels of type |
n_folds |
if a integer value k>0 is specified, a k-fold cross validation on the training data is performed to assess the quality of the model: the accuracy rate for classification and the Mean Squared Error for regression |
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".
|
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 | library(MlBayesOpt)
set.seed(71)
res0 <- svm_cv_opt(data = iris,
label = Species,
n_folds = 3,
init_points = 10,
n_iter = 1)
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