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Machine learning (ML) for classification and prediction based on a set of
features is used to make decisions in healthcare, economics, criminal justice
and more. However, implementing an ML pipeline including preprocessing, model
selection, and evaluation can be time-consuming, confusing, and difficult. Here,
we present mikropml
(pronounced
"meek-ROPE em el"), an easy-to-use R package that implements ML pipelines using
regression, support vector machines, decision trees, random forest, or
gradient-boosted trees. The package is available on
GitHub,
CRAN, and
conda.
Most applications of machine learning (ML) require reproducible steps for data pre-processing, cross-validation, testing, model evaluation, and often interpretation of why the model makes particular predictions. Performing these steps is important, as failure to implement them can result in incorrect and misleading results [@teschendorff_avoiding_2019; @wiens_no_2019].
Supervised ML is widely used to recognize patterns in large datasets and to make
predictions about outcomes of interest. Several packages including caret
[@kuhn_building_2008] and tidymodels
[@kuhn_tidymodels_2020] in R,
scikitlearn
[@pedregosa_scikit-learn_2011] in Python, and the H2O autoML
platform [@h2o_platform] allow scientists to train ML models with a variety of
algorithms. While these packages provide the tools necessary for each ML step,
they do not implement a complete ML pipeline according to good practices in the
literature. This makes it difficult for practitioners new to ML to easily begin
to perform ML analyses.
To enable a broader range of researchers to apply ML to their problem domains,
we created mikropml
, an easy-to-use
R package [@r_core_team_r_2020] that implements the ML pipeline created by
Topçuoğlu et al. [@topcuoglu_framework_2020] in a single function that returns a trained model,
model performance metrics and feature importance. mikropml
leverages
the caret
package to support several ML algorithms: linear regression,
logistic regression, support vector machines with a radial basis kernel,
decision trees, random forest, and gradient boosted trees. It incorporates good
practices in ML training, testing, and model evaluation
[@topcuoglu_framework_2020;@teschendorff_avoiding_2019]. Furthermore, it
provides data preprocessing steps based on the FIDDLE (FlexIble Data-Driven
pipeLinE) framework outlined in Tang et al. [@tang_democratizing_2020] and
post-training permutation importance steps to estimate the importance of each
feature in the models trained [@breiman_random_2001; @fisher_all_2018].
mikropml
can be used as a starting point in the application of ML to datasets
from many different fields. It has already been applied to microbiome data to
categorize patients with colorectal cancer [@topcuoglu_framework_2020], to
identify differences in genomic and clinical features associated with bacterial
infections [@lapp_machine_2020], and to predict gender-based biases in academic
publishing [@hagan_women_2020].
The mikropml
package includes functionality to preprocess the data, train ML
models, evaluate model performance, and quantify feature importance (Figure 1).
We also provide
vignettes and an
example Snakemake
workflow
[@koster_snakemakescalable_2012] to showcase how to run an ideal ML pipeline
with multiple different train/test data splits. The results can be visualized
using helper functions that use ggplot2
[@wickham_ggplot2_2016].
While mikropml allows users to get started quickly and facilitates reproducibility, it is not a replacement for understanding the ML workflow which is still necessary when interpreting results [@pollard_turning_2019]. To facilitate understanding and enable one to tailor the code to their application, we have heavily commented the code and have provided supporting documentation which can be read online.
We provide the function preprocess_data()
to preprocess features using several
different functions from the caret
package. preprocess_data()
takes
continuous and categorical data, re-factors categorical data into binary
features, and provides options to normalize continuous data, remove features
with near-zero variance, and keep only one instance of perfectly correlated
features. We set the default options based on those implemented in FIDDLE
[@tang_democratizing_2020]. More details on how to use preprocess_data()
can
be found in the accompanying
vignette.
The main function in mikropml, run_ml()
, minimally takes in the model choice
and a data frame with an outcome column and feature columns. For model choice,
mikropml
currently supports logistic and linear regression [glmnet
:
@friedman_regularization_2010], support vector machines with a radial basis
kernel [kernlab
: @karatzoglou_kernlab_2004], decision trees [rpart
:
@therneau_rpart_2019], random forest [randomForest
: @liaw_classication_2002],
and gradient-boosted trees [xgboost
: @chen_xgboost_2020]. run_ml()
randomly
splits the data into train and test sets while maintaining the distribution of
the outcomes found in the full dataset. It also provides the option to split the
data into train and test sets based on categorical variables (e.g. batch,
geographic location, etc.). mikropml
uses the caret
package
[@kuhn_building_2008] to train and evaluate the models, and optionally
quantifies feature importance. The output includes the best model built based on
tuning hyperparameters in an internal and repeated cross-validation step, model
evaluation metrics, and optional feature importances. Feature importances are
calculated using a permutation test, which breaks the relationship between the
feature and the true outcome in the test data, and measures the change in model
performance. This provides an intuitive metric of how individual features
influence model performance and is comparable across model types, which is
particularly useful for model interpretation [@topcuoglu_framework_2020]. Our
introductory
vignette
contains a comprehensive tutorial on how to use run_ml()
.
{width=100%}
To investigate the variation in model performance depending on the train and
test set used [@topcuoglu_framework_2020; @lapp_machine_2020], we provide
examples of how to run_ml()
many times with different train/test splits and
how to get summary information about model performance on a local
computer or on a
high-performance computing cluster using a Snakemake
workflow.
One particularly important aspect of ML is hyperparameter tuning. We provide a
reasonable range of default hyperparameters for each model type. However
practitioners should explore whether that range is appropriate for their data,
or if they should customize the hyperparameter range. Therefore, we provide a
function plot_hp_performance()
to plot the cross-validation performance metric
of a single model or models built using different train/test splits. This helps
evaluate if the hyperparameter range is being searched exhaustively and allows
the user to pick the ideal set. We also provide summary plots of test
performance metrics for the many train/test splits with different models using
plot_model_performance()
. Examples are described in the accompanying vignette
on hyperparameter
tuning.
mikropml is written in R [@r_core_team_r_2020] and depends on several packages:
dplyr
[@wickham_dplyr_2020], rlang
[@henry_rlang_2020] and caret
[@kuhn_building_2008]. The ML algorithms supported by mikropml
require:
glmnet
[@friedman_regularization_2010], e1071
[@meyer_e1071_2020], and
MLmetrics
[@yan_mlmetrics_2016] for logistic regression, rpart2
[@therneau_rpart_2019] for decision trees, randomForest
[@liaw_classication_2002] for random forest, xgboost
[@chen_xgboost_2020] for
xgboost, and kernlab
[@karatzoglou_kernlab_2004] for support vector machines.
We also allow for parallelization of cross-validation and other steps using the
foreach
, doFuture
, future.apply
, and future
packages
[@bengtsson_futureapply_2020]. Finally, we use ggplot2
for plotting
[@wickham_ggplot2_2016].
We thank members of the Schloss Lab who participated in code clubs related to the initial development of the pipeline, made documentation improvements, and provided general feedback. We also thank Nick Lesniak for designing the mikropml logo.
We thank the US Research Software Sustainability Institute (NSF #1743188) for providing training to KLS at the Winter School in Research Software Engineering.
Salary support for PDS came from NIH grant 1R01CA215574. KLS received support from the NIH Training Program in Bioinformatics (T32 GM070449). ZL received support from the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE 1256260. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
BDT, ZL, and KLS contributed equally. Author order among the co-first authors was determined by time since joining the project.
BDT, ZL, and KLS conceptualized the study and wrote the code. KLS structured the code in R package form. BDT, ZL, JW, and PDS developed methodology. PDS, ES, and JW supervised the project. BDT, ZL, and KLS wrote the original draft. All authors reviewed and edited the manuscript.
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