klassets

The {klassets} package is a collection of functions to simulate data sets to:

• Teach how some Statistics Models and Machine Learning algorithms works.
• Illustrate certain some particular events such as heteroskedasticity or the Simpson’s paradox.
• Compare the predictions between models, for example logistic regression vs decision tree vs -Nearest Neighbours.

Some examples

Don’t forget to visualize the data

library(klassets)

set.seed(123)

df <- sim_quasianscombe_set_1(beta0 = 3, beta1 = 0.5)

plot(df) +
  ggplot2::labs(subtitle = "Very similar to the given parameters (3 and 0.5)")

library(patchwork)

df2 <- sim_quasianscombe_set_2(df, fun = sin)
df6 <- sim_quasianscombe_set_6(df, groups = 2, b1_factor = -1)

plot(df2) + plot(df6)

Compare models in a classifications task

df <- sim_response_xy(relationship = function(x, y) sin(x*pi) > sin(y*pi))

df
#> # A tibble: 500 × 3
#>    response       x       y
#>    <fct>      <dbl>   <dbl>
#>  1 FALSE    -0.681   0.707 
#>  2 FALSE    -0.711   0.332 
#>  3 FALSE    -0.702   0.467 
#>  4 TRUE      0.0289 -0.371 
#>  5 TRUE     -0.0143  0.335 
#>  6 TRUE      0.233  -0.0722
#>  7 FALSE    -0.105   0.301 
#>  8 FALSE    -0.889   0.572 
#>  9 FALSE    -0.989   0.803 
#> 10 FALSE    -0.556   0.0548
#> # … with 490 more rows

plot(df)

You can fit different models and see how the predictions are made.

plot(fit_logistic_regression(df, order = 4)) +
plot(fit_classification_tree(df))            +
plot(fit_classification_random_forest(df))   +
plot(fit_knn(df))                            +
  plot_layout(guides = "collect")

How -means works

Another example of what can be done with {klassets}.

Where to start

You can check:

• vignette("Quasi-Anscombe-data-sets") to know more about sim_quasianscombe_set* functions family.
• vignette("Binary-classification")/vignette("Regression") to see classifiers/regression models/methods.
• vignette("Clustering") to see clustering functions.
• vignette("MNIST") to work with this data set to compare models and check some variable importance metrics.

Installation

You can install the development version of klassets from GitHub with:

# install.packages("remotes")
remotes::install_github("jbkunst/klassets")

Extra Info(?!)

Why the name Klassets? Just a weird merge for Class/Klass and sets.

Some inspiration and similar ideas:

• https://jumpingrivers.github.io/datasauRus/
• https://eliocamp.github.io/metamer/
• http://www.econometricsbysimulation.com/2019/03/the-importance-of-graphing-your-data.html This is almost the same, but the approach it’s different.