/
_pkgdown.yml
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/
_pkgdown.yml
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url: http://www.schlosslab.org/mikropml
destination: docs
development:
mode: auto
template:
bootstrap: 5
articles:
- title: Paper
navbar: Paper
contents:
- paper
- title: Vignettes
navbar: Vignettes
contents:
- introduction
- preprocess
- tuning
- parallel
authors:
Begüm Topçuoğlu:
href: "https://github.com/BTopcuoglu"
Zena Lapp:
href: "https://github.com/zenalapp"
Kelly Sovacool:
href: "https://github.com/kelly-sovacool"
Patrick Schloss:
href: "https://github.com/pschloss"
home:
sidebar:
structure: [links, license, community, citation, authors, dev, custom]
components:
custom:
title: Impact metrics
text: >
<br>
<script async src="https://badge.dimensions.ai/badge.js" charset="utf-8"></script>
<script type='text/javascript' src='https://d1bxh8uas1mnw7.cloudfront.net/assets/embed.js'></script>
<span class="__dimensions_badge_embed__" data-doi="10.21105/joss.03073" data-style="small_circle"></span>
<br>
<div data-badge-type="donut" data-doi="10.21105/joss.03073" data-condensed="true" data-badge-popover="right" class="altmetric-embed"></div>
reference:
- title: Main
desc: >
The foundations for training machine learning models.
contents:
- mikropml
- preprocess_data
- run_ml
- title: Model evaluation
desc: >
Evaluate and interpret models.
contents:
- get_feature_importance
- get_performance_tbl
- sensspec
- calc_mean_perf
- calc_baseline_precision
- calc_balanced_precision
- compare_models
- permute_p_value
- bootstrap_performance
- title: Plotting helpers
desc: >
Visualize results to help you tune hyperparameters and choose model methods.
contents:
- starts_with('plot')
- tidy_perf_data
- get_hp_performance
- combine_hp_performance
- title: Package Data
- subtitle: datasets
contents:
- otu_small
- otu_mini_bin
- otu_mini_multi
- otu_mini_multi_group
- otu_data_preproc
- subtitle: ML results
contents:
- contains("results")
- subtitle: misc
contents:
- otu_mini_cv
- replace_spaces
- title: Pipeline customization
desc: >
Customize various steps of the pipeline beyond the arguments provided by
run_ml() and preprocess_data().
contents:
- remove_singleton_columns
- get_caret_processed_df
- randomize_feature_order
- get_partition_indices
- get_outcome_type
- get_hyperparams_list
- get_tuning_grid
- define_cv
- get_perf_metric_name
- get_perf_metric_fn
- train_model
- calc_perf_metrics
- group_correlated_features