Function reference • mikropml

Main The foundations for training machine learning models.
`mikropml`	mikropml: User-Friendly R Package for Robust Machine Learning Pipelines
`preprocess_data()`	Preprocess data prior to running machine learning
`run_ml()`	Run the machine learning pipeline
Plotting helpers Visualize performance to help you tune hyperparameters and choose model methods.
`plot_hp_performance()`	Plot hyperparameter performance metrics
`plot_model_performance()`	Plot performance metrics for multiple ML runs with different parameters
`tidy_perf_data()`	Tidy the performance dataframe
`get_hp_performance()`	Get hyperparameter performance metrics
`combine_hp_performance()`	Combine hyperparameter performance metrics for multiple train/test splits
Package Data
datasets
`otu_small`	Small OTU abundance dataset
`otu_mini_bin`	Mini OTU abundance dataset
`otu_mini_multi`	Mini OTU abundance dataset with 3 categorical variables
`otu_mini_multi_group`	Groups for otu_mini_multi
ML results
`otu_mini_bin_results_glmnet`	Results from running the pipline with L2 logistic regression on `otu_mini_bin` with feature importance and grouping
`otu_mini_bin_results_rf`	Results from running the pipline with random forest on `otu_mini_bin`
`otu_mini_bin_results_rpart2`	Results from running the pipline with rpart2 on `otu_mini_bin`
`otu_mini_bin_results_svmRadial`	Results from running the pipline with svmRadial on `otu_mini_bin`
`otu_mini_bin_results_xgbTree`	Results from running the pipline with xbgTree on `otu_mini_bin`
`otu_mini_cont_results_glmnet`	Results from running the pipeline with glmnet on `otu_mini_bin` with `Otu00001` as the outcome
`otu_mini_cont_results_nocv`	Results from running the pipeline with glmnet on `otu_mini_bin` with `Otu00001` as the outcome column, using a custom train control scheme that does not perform cross-validation
`otu_mini_multi_results_glmnet`	Results from running the pipeline with glmnet on `otu_mini_multi` for multiclass outcomes
misc
`otu_mini_cv`	Cross validation on `train_data_mini` with grouped features.
`replace_spaces()`	Replace spaces in all elements of a character vector with underscores
Pipeline customization These are functions called by preprocess_data() or run_ml(). We make them available in case you would like to customize various steps of the pipeline beyond the arguments provided by the main functions.
`remove_singleton_columns()`	Remove columns appearing in only `threshold` row(s) or fewer.
`get_caret_processed_df()`	Get preprocessed dataframe for continuous variables
`randomize_feature_order()`	Randomize feature order to eliminate any position-dependent effects
`get_partition_indices()`	Select indices to partition the data into training & testing sets.
`get_outcome_type()`	Get outcome type.
`get_hyperparams_list()`	Set hyperparameters based on ML method and dataset characteristics
`get_tuning_grid()`	Generate the tuning grid for tuning hyperparameters
`define_cv()`	Define cross-validation scheme and training parameters
`get_perf_metric_name()`	Get default performance metric name
`get_perf_metric_fn()`	Get default performance metric function
`train_model()`	Train model using `caret::train()`.
`calc_perf_metrics()`	Get performance metrics for test data
`get_performance_tbl()`	Get model performance metrics as a one-row tibble
`get_feature_importance()`	Get feature importance using the permutation method
`group_correlated_features()`	Group correlated features

Reference

Main

Plotting helpers

Package Data

datasets

ML results

misc

Pipeline customization