plot_model_performance: Plot performance metrics for multiple ML runs with different...

In SchlossLab/mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

Plot performance metrics for multiple ML runs with different parameters

Description

ggplot2 is required to use this function.

Usage

plot_model_performance(performance_df)

Arguments

performance_df

dataframe of performance results from multiple calls to run_ml()

Value

A ggplot2 plot of performance.

Author(s)

Begüm Topçuoglu, [e-mail]

Kelly Sovacool, [e-mail]

Examples

## Not run: 
# call `run_ml()` multiple times with different seeds
results_lst <- lapply(seq(100, 104), function(seed) {
  run_ml(otu_small, "glmnet", seed = seed)
})
# extract and combine the performance results
perf_df <- lapply(results_lst, function(result) {
  result[["performance"]]
}) %>%
  dplyr::bind_rows()
# plot the performance results
p <- plot_model_performance(perf_df)


# call `run_ml()` with different ML methods
param_grid <- expand.grid(
  seeds = seq(100, 104),
  methods = c("glmnet", "rf")
)
results_mtx <- mapply(
  function(seed, method) {
    run_ml(otu_mini_bin, method, seed = seed, kfold = 2)
  },
  param_grid$seeds, param_grid$methods
)
# extract and combine the performance results
perf_df2 <- dplyr::bind_rows(results_mtx["performance", ])
# plot the performance results
p <- plot_model_performance(perf_df2)

# you can continue adding layers to customize the plot
p +
  theme_classic() +
  scale_color_brewer(palette = "Dark2") +
  coord_flip()

## End(Not run)

SchlossLab/mikropml documentation built on Aug. 24, 2023, 9:51 p.m.