stacks is an R package for model stacking that aligns with the tidymodels. Model stacking is an ensembling method that takes the outputs of many models and combines them to generate a new model—referred to as an ensemble in this package—that generates predictions informed by each of its members.
The process goes something like this:
You can install the package with the following code:
Install the development version with:
remotes::install_github("tidymodels/stacks", ref = "main")
stacks is generalized with respect to:
stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out h2o and SuperLearner.
Rather than diving right into the implementation, we’ll focus here on
how the pieces fit together, conceptually, in building an ensemble with
stacks. See the
basics vignette for an example of the API in action!
At the highest level, ensembles are formed from model definitions. In this package, model definitions are an instance of a minimal workflow, containing a model specification (as defined in the parsnip package) and, optionally, a preprocessor (as defined in the recipes package). Model definitions specify the form of candidate ensemble members.
To be used in the same ensemble, each of these model definitions must
share the same resample. This
rset object, when paired
with the model definitions, can be used to generate the tuning/fitting
results objects for the candidate ensemble members with tune.
Candidate members first come together in a
data_stack object through
add_candidates() function. Principally, these objects are just
tibbles, where the first column gives
the true outcome in the assessment set (the portion of the training set
used for model validation), and the remaining columns give the
predictions from each candidate ensemble member. (When the outcome is
numeric, there’s only one column per candidate ensemble member.
Classification requires as many columns per candidate as there are
levels in the outcome variable.) They also bring along a few extra
attributes to keep track of model definitions.
Then, the data stack can be evaluated using
determine to how best to combine the outputs from each of the candidate
members. In the stacking literature, this process is commonly called
The outputs of each member are likely highly correlated. Thus, depending on the degree of regularization you choose, the coefficients for the inputs of (possibly) many of the members will zero out—their predictions will have no influence on the final output, and those terms will thus be thrown out.
These stacking coefficients determine which candidate ensemble members
will become ensemble members. Candidates with non-zero stacking
coefficients are then fitted on the whole training set, altogether
making up a
This model stack object, outputted from
fit_members(), is ready to
predict on new data! The trained ensemble members are often referred to
as base models in the stacking literature.
The full visual outline for these steps can be found
The API for the package closely mirrors these ideas. See the
vignette for an example of how this grammar is implemented!
This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
If you think you have encountered a bug, please submit an issue.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
In the stacks package, some test objects take too long to build with
every commit. If your contribution changes the structure of
model_stacks objects, please regenerate these test objects by
running the scripts in
man-roxygen/example_models.Rmd, including those
with chunk options
eval = FALSE.
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