# sklearn Pipe Step Interface for vtreat

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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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