run_ml: Run the machine learning pipeline
GITHUB
SchlossLab/mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines
('introduction') for more details.
Usage
run_ml(
('introduction') for more details.
Usage
run_ml(
('introduction') for more details.
Usage
run_ml(
('introduction') for more details.
Usage
run_ml(
",
"E", "A", "C", "E", "F", "A"
test_that("run_ml works for logistic regression", {
",
"E", "A", "C", "E", "F", "A"
test_that("run_ml works for logistic regression", {
= FALSE------------------------------------------------------------
# results <- run_ml(otu_mini_bin,
# "glmnet
",
"E", "A", "C", "E", "F", "A"
test_that("run_ml works for logistic regression", {
of performance results from multiple calls to run_ml()
Value
Tidy dataframe with model performance metrics.
of performance results from multiple calls to run_ml()
Value
Tidy dataframe with model performance metrics.
this function.
#'
#' @param performance_df dataframe of performance results from multiple calls to `run_ml()`
this function.
#'
#' @param performance_df dataframe of performance results from multiple calls to `run_ml()`
multiple calls to run_ml()
Value
A ggplot2 plot of performance.
multiple calls to run_ml()
Value
A ggplot2 plot of performance.
of performance results from multiple calls to run_ml()
Value
Tidy dataframe with model performance metrics.
pipelines. All you
need to run the ML pipeline is one function: `run_ml()`. We've selected sensible
default arguments
pipelines. All you
need to run the ML pipeline is one function: `run_ml()`. We've selected sensible
default arguments
pipelines. All you
need to run the ML pipeline is one function: `run_ml()`. We've selected sensible
default arguments
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.
