bark (version 1.0.5)

bark-package: bark: Bayesian Additive Regression Trees

Description

Implementation of Bayesian Additive Regression Kernels with Feature Selection for Nonparametric Regression for Gaussian regression and classification for binary Probit models

_PACKAGE

Details

BARK is a Bayesian sum-of-kernels model or because of the Bayesian priors is a Bayesian Additive Regression Kernel model.
For numeric response \(y\), we have \(y = f(x) + \epsilon\), where \(\epsilon \sim N(0,\sigma^2)\).
For a binary response \(y\), \(P(Y=1 | x) = F(f(x))\), where \(F\) denotes the standard normal cdf (probit link). In both cases, \(f\) is the sum of many Gaussian kernel functions. The goal is to have very flexible inference for the unknown function \(f\). bark uses an approximated Cauchy process as the prior distribution for the unknown function \(f\). Feature selection can be achieved through the inference on the scale parameters in the Gaussian kernels. BARK accepts four different types of prior distributions through setting values for selection (TRUE or FALSE), which allows scale parameters for some variables to be set to zero, removing the variables from the kernels selection = TRUE; this enables either soft shrinkage or hard shrinkage for the scale parameters. The input common_lambdas (TRUE or FALSE) specifies whether a common scale parameter should be used for all predictors (TRUE) or if FALSE allows the scale parameters to differ across all variables in the kernel.

References

Ouyang, Zhi (2008) Bayesian Additive Regression Kernels. Duke University. PhD dissertation, Chapter 3.

See Also

Other bark functions: bark(), bark-package-deprecated, sim_Friedman1(), sim_Friedman2(), sim_Friedman3(), sim_circle()

Examples

# \donttest{
 # Simulate regression example
 # Friedman 2 data set, 200 noisy training, 1000 noise free testing
 # Out of sample MSE in SVM (default RBF): 6500 (sd. 1600)
 # Out of sample MSE in BART (default):    5300 (sd. 1000)
 traindata <- sim_Friedman2(200, sd=125)
 testdata <- sim_Friedman2(1000, sd=0)
 fit.bark.d <- bark(y ~ ., data = data.frame(traindata),
                    testdata = data.frame(testdata), 
                    classification = FALSE,
                    selection = FALSE,
                    common_lambdas = TRUE)
 boxplot(as.data.frame(fit.bark.d$theta.lambda))
 mean((fit.bark.d$yhat.test.mean-testdata$y)^2)
 # Simulate classification example
 # Circle 5 with 2 signals and three noisy dimensions
 # Out of sample erorr rate in SVM (default RBF): 0.110 (sd. 0.02)
 # Out of sample error rate in BART (default):    0.065 (sd. 0.02)
 traindata <- sim_circle(200, dim=5)
 testdata <- sim_circle(1000, dim=5)
 fit.bark.se <- bark(y ~ ., data= data.frame(traindata), 
                     testdata= data.frame(testdata),
                     classification=TRUE, 
                     selection=TRUE,
                     common_lambdas = FALSE)
                   
 boxplot(as.data.frame(fit.bark.se$theta.lambda))
 mean((fit.bark.se$yhat.test.mean>0)!=testdata$y)
# }