svm_opt: Bayesian Optimization for SVM

In MlBayesOpt: Hyper Parameter Tuning for Machine Learning, Using Bayesian Optimization

Description

This function estimates parameters for SVM(Gaussian Kernel) based on bayesian optimization

Usage

svm_opt(train_data, train_label, test_data, test_label,
  gamma_range = c(10^(-3), 10^1), cost_range = c(10^(-2), 10^2),
  svm_kernel = "radial", degree_range = c(3L, 10L),
  coef0_range = c(10^(-1), 10^1), init_points = 4, n_iter = 10,
  acq = "ei", kappa = 2.576, eps = 0, optkernel = list(type =
  "exponential", power = 2))

Arguments

train_data	A data frame for training of SVM
train_label	The column of class to classify in the training data
test_data	A data frame for training of SVM
test_label	The column of class to classify in the test data
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. linear: u'v polynomial: (γ u'v +coef0)^{degree} radial basis: exp(-γ|u-v|^2) sigmoid: tanh(γ u'v + coef0)
degree_range	Parameter needed for kernel of type polynomial. Default is c(3L, 10L)
coef0_range	parameter needed for kernels of type polynomial and sigmoid. Default is c(10 ^ (-1), 10 ^ 1)
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". ucb GP Upper Confidence Bound ei Expected Improvement poi Probability of Improvement
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

Value

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

Examples

library(MlBayesOpt)

set.seed(71)
res0 <- svm_opt(train_data = iris_train,
                train_label = Species,
                test_data = iris_test,
                test_label = Species,
                svm_kernel = "polynomial",
                init_points = 10,
                n_iter = 1)

MlBayesOpt documentation built on May 2, 2019, 9:20 a.m.