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We’ve been experimenting with this for a while, and the next R vtreat package will have a back-port of the Python vtreat package sklearn pipe step interface (in addition to the standard R interface).
This means the user can express easily express modeling intent by choosing between coder$fit_transform(train_data)
, coder$fit(train_data_cal)$transform(train_data_model)
, and coder$fit(application_data)
.
We have also regenerated the current task-oriented vtreat documentation to demonstrate the new nested bias warning feature:
- Regression:
R
regression example,Python
regression example. - Classification:
R
classification example,Python
classification example. - Unsupervised data preparation:
R
unsupervised example,Python
unsupervised example. - Multinomial classification:
R
multinomial classification example,Python
multinomial classification example.
And we now have new versions of these documents showing the sklearn $fit_transform()
style notation in R.
- Regression:
R
$fit_transform()
regression example. - Classification:
R
$fit_transform()
classification example. - Unsupervised data preparation:
R
$fit_transform()
unsupervised example. - Multinomial classification:
R
$fit_transform()
multinomial classification example.
The original R interface is going to remain the standard interface for vtreat. It is more idiomatic R, and is taught in chapter 8 of Zumel, Mount; Practical Data Science with R, 2nd Edition, Manning 2019.
In contrast, the $fit_transform()
notation will always just be an adaptor over the primary R interface. However, there is a lot to be learned from sklearn’s organization and ideas, so we felt we would use make their naming convention available as a way of showing appreciation and giving credit. Some more of my notes on the grace of the sklearn interface in being a good way to manage state and generative effects (see Brendan Fong, David I. Spivak; An Invitation to Applied Category Theory, Cambridge University Press, 2019) can be found here.
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