Hi,
I have a mixture of table data and intermediate vectors from another
model, which don't seem to scale productively. The fact that
MinMaxScaler seems to do all features in X makes me wonder if/how people
train with such mixed data.
The easy approaches seem to be either scale the db data and then combine
with the vectors, or just scale the db columns in place 'by hand'.
Otherwise, I might consider adding a column-list option to the API.
I suspect I'm just missing something important, since I wandered in
following this purely-tabular example, which seemed good before adding
ML-derived vectors:
https://www.kaggle.com/code/carlmcbrideellis/tabular-classification-with-ne…
Any advice or more-appropriate example to follow would be great.
Thanks,
Bill
--
--
Phobrain.com
Hello everyone,
We're happy to announce the 1.6.1 release !
It contains fixes for a few regressions introduced in 1.6.
You can see the changelog here:
https://scikit-learn.org/stable/whats_new/v1.6.html#version-1-6-1
You can upgrade with pip as usual:
```
pip install -U scikit-learn
```
The conda-forge builds can be installed using:
```
conda install -c conda-forge scikit-learn
```
Thanks to everyone who contributed to this release !
Jérémie, on behalf of the Scikit-learn maintainers team.
