sklearn Pipe Step Interface for vtreat
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).
New vtreat Feature: Nested Model Bias Warning
For quite a while we have been teaching estimating variable re-encodings on the exact same data they are later naively using to train a model on, leads to an undesirable nested model bias. The vtreat package (both the R version and Python version) both incorporate a cross-frame method that allows […]
PyData Los Angeles 2019 talk: Preparing Messy Real World Data for Supervised Machine Learning
Video of our PyData Los Angeles 2019 talk Preparing Messy Real World Data for Supervised Machine Learning is now available. In this talk describe how to use vtreat, a package available in R and in Python, to correctly re-code real world data for supervised machine learning tasks. Please check it […]
Slides for PyData LA 2019 vtreat Talk
Slides for PyData LA 2019 vtreat Talk are here!
Nina Zumel and John Mount speaking on vtreat at PyData LA 2019
As we have announced before, we have ported the R version of vtreat to a new Python version of vtreat. Our latest news is: we are speaking about the Python version at PyData LA 2019 (Thursday 10:50 AM–11:35 AM in Track 2 Room).
When Cross-Validation is More Powerful than Regularization
Regularization is a way of avoiding overfit by restricting the magnitude of model coefficients (or in deep learning, node weights). A simple example of regularization is the use of ridge or lasso regression to fit linear models in the presence of collinear variables or (quasi-)separation. The intuition is that smaller […]
New vtreat Documentation (Starting with Multinomial Classification)
Nina Zumel finished some great new documentation showing how to use Python vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to R vtreat. So we now have good introductions on how to use vtreat to prepare data for the common tasks of: […]
Preparing Data for Supervised Classification
Nina Zumel has been polishing up new vtreat for Python documentation and tutorials. They are coming out so good that I find to be fair to the R community I must start to back-port this new documentation to vtreat for R.
New Getting Started with vtreat Documentation
Win Vector LLC‘s Dr. Nina Zumel has just released some new vtreat documentation. vtreat is a an all-in one step data preparation system that helps defend your machine learning algorithms from: Missing values Large cardinality categorical variables Novel levels from categorical variables I hoped she could get the Python vtreat […]
vtreat up on PyPi
I am excited to announce vtreat is now available for Python on PyPi, in addition for R on CRAN.