# User-Friendly R Package for Supervised Machine Learning Pipelines

## Documentation for package ‘mikropml’ version 1.2.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

calc_perf_metrics | Get performance metrics for test data
combine_hp_performance | Combine hyperparameter performance metrics for multiple train/test splits
define_cv | Define cross-validation scheme and training parameters
get_caret_processed_df | Get preprocessed dataframe for continuous variables
get_feature_importance | Get feature importance using the permutation method
get_hp_performance | Get hyperparameter performance metrics
get_hyperparams_list | Set hyperparameters based on ML method and dataset characteristics
get_outcome_type | Get outcome type.
get_partition_indices | Select indices to partition the data into training & testing sets.
get_performance_tbl | Get model performance metrics as a one-row tibble
get_perf_metric_fn | Get default performance metric function
get_perf_metric_name | Get default performance metric name
get_tuning_grid | Generate the tuning grid for tuning hyperparameters
group_correlated_features | Group correlated features
mikropml | mikropml: User-Friendly R Package for Robust Machine Learning Pipelines
otu_mini_bin | Mini OTU abundance dataset
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_cv | Cross validation on 'train_data_mini' with grouped features.
otu_mini_multi | Mini OTU abundance dataset with 3 categorical variables
otu_mini_multi_group | Groups for otu_mini_multi
otu_mini_multi_results_glmnet | Results from running the pipeline with glmnet on 'otu_mini_multi' for multiclass outcomes
otu_small | Small OTU abundance dataset
plot_hp_performance | Plot hyperparameter performance metrics
plot_model_performance | Plot performance metrics for multiple ML runs with different parameters
preprocess_data | Preprocess data prior to running machine learning
randomize_feature_order | Randomize feature order to eliminate any position-dependent effects
remove_singleton_columns | Remove columns appearing in only 'threshold' row(s) or fewer.
replace_spaces | Replace spaces in all elements of a character vector with underscores
run_ml | Run the machine learning pipeline
tidy_perf_data | Tidy the performance dataframe
train_model | Train model using 'caret::train()'.