Bayesian

Introduction

The tisthemachinelearner package provides a simple R interface to scikit-learn models through Python’s tisthemachinelearner package. This vignette demonstrates how to use the package with R’s built-in mtcars dataset.

Setup

First, let’s load the required packages:

library(reticulate)
library(tisthemachinelearner)
#> Loading required package: Matrix
#> To use this package:
#>   1. Create venv: uv venv venv
#>   2. Activate: source venv/bin/activate

Data Preparation

We’ll use the classic mtcars dataset to predict miles per gallon (mpg) based on other car characteristics:

# Load data
data(mtcars)
head(mtcars)

# Split features and target
X <- as.matrix(mtcars[, -1])  # all columns except mpg
y <- mtcars[, 1]              # mpg column

# Create train/test split
set.seed(42)
train_idx <- sample(nrow(mtcars), size = floor(0.8 * nrow(mtcars)))
X_train <- X[train_idx, ]
X_test <- X[-train_idx, ]
y_train <- y[train_idx]
y_test <- y[-train_idx]

# R6 interface
model <- Regressor$new(model_name = "BayesianRidge", venv_path = "../venv")
start <- proc.time()[3]
model$fit(X_train, y_train)
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

start <- proc.time()[3]
preds <- model$predict(X_test, method="bayesian")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
print(preds)

model <- Regressor$new(model_name = "ARDRegression", venv_path = "../venv")
start <- proc.time()[3]
model$fit(X_train, y_train)
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

start <- proc.time()[3]
preds <- model$predict(X_test, method="bayesian")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
print(preds)


# S3 interface
start <- proc.time()[3]
model <- regressor(X_train, y_train, model_name = "GaussianProcessRegressor", venv_path = "../venv")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")

start <- proc.time()[3]
preds <- predict(model, X_test, method="bayesian")
end <- proc.time()[3]
cat("Time taken:", end - start, "seconds\n")
print(preds)

Conclusion

This example demonstrates how to:

  1. Prepare R data for use with the regressor
  2. Fit different types of regression models
  3. Make predictions on new data
  4. Calculate and compare model performance
  5. Visualize results

The tisthemachinelearner package makes it easy to use scikit-learn models with R data, combining the familiarity of R data structures with the power of Python’s machine learning ecosystem.

Session Info

sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] tisthemachinelearner_0.10.0 Matrix_1.7-5               
#> [3] reticulate_1.46.0           rmarkdown_2.31             
#> 
#> loaded via a namespace (and not attached):
#>  [1] digest_0.6.39    R6_2.6.1         fastmap_1.2.0    xfun_0.57       
#>  [5] lattice_0.22-9   maketools_1.3.2  cachem_1.1.0     knitr_1.51      
#>  [9] htmltools_0.5.9  png_0.1-9        buildtools_1.0.0 lifecycle_1.0.5 
#> [13] cli_3.6.6        grid_4.6.0       sass_0.4.10      jquerylib_0.1.4 
#> [17] compiler_4.6.0   sys_3.4.3        tools_4.6.0      evaluate_1.0.5  
#> [21] bslib_0.11.0     Rcpp_1.1.1-1.1   yaml_2.3.12      jsonlite_2.0.0  
#> [25] rlang_1.2.0