A big thank you to Dmytro Perepolkin for sharing a “Keep Calm and Use vtreat” poster!

Also, we have translated the Python vtreat steps from our recent “Cross-Methods are a Leak/Variance Trade-Off” article into R vtreat steps here.

This R-port demonstrates the new to R fit/prepare notation!

We want vtreat to be a platform agnostic (works in R, works in Python, works elsewhere) well documented standard methodology.

To this end: Nina and I have re-organized the basic vtreat use documentation as follows:

• Regression: R regression example, fit/prepare
  interface,
  R regression example, design/prepare/experiment
  interface,
  Python regression
  example.
• Classification: R classification example, fit/prepare
  interface,
  R classification example, design/prepare/experiment
  interface,
  Python classification
  example.
• Unsupervised tasks: R unsupervised example, fit/prepare
  interface,
  R unsupervised example, design/prepare/experiment
  interface,
  Python unsupervised
  example.
• Multinomial classification: R multinomial classification
  example, fit/prepare
  interface,
  R multinomial classification example, design/prepare/experiment
  interface,
  Python multinomial classification
  example.

Categories: Administrativia Pragmatic Data Science Tutorials

Tagged as: data science Practical Data Science Pragmatic Machine Learning pydata R Statistics

jmount

Data Scientist and trainer at Win Vector LLC. One of the authors of Practical Data Science with R.