
Plot performance metrics for multiple ML runs with different parameters
Source:R/plot.R
plot_model_performance.Rd
ggplot2 is required to use this function.
Arguments
- performance_df
dataframe of performance results from multiple calls to
run_ml()
Examples
if (FALSE) {
# 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()
}