Use make_column_selector where appropriate. #92
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glemaitre
approved these changes
Nov 17, 2020
| 'workclass', 'education', 'marital-status', 'occupation', | ||
| 'relationship', 'race', 'native-country', 'sex'] | ||
| categorical_columns_selector = selector(dtype_include=object) | ||
| categorical_columns = categorical_columns_selector(data) |
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Maybe we can split using a new cell here just to show the output of using the selector?
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I guess we do it already in some notebooks previously e.g. here:
https://inria.github.io/scikit-learn-mooc/python_scripts/03_categorical_pipeline.html#working-with-categorical-variables
I am wondering whether it is better to do in all notebooks or only in one of the beginning at the beginning.
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Yep it is true that it is the fourth notebook.
This is also fine.
…On Tue, 17 Nov 2020 at 15:41, Loïc Estève ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In python_scripts/04_parameter_tuning_search.py
<#92 (comment)>
:
> from sklearn.preprocessing import OrdinalEncoder
-categorical_columns = [
- 'workclass', 'education', 'marital-status', 'occupation',
- 'relationship', 'race', 'native-country', 'sex']
+categorical_columns_selector = selector(dtype_include=object)
+categorical_columns = categorical_columns_selector(data)
I guess we do it already in some notebooks previously e.g. here:
https://inria.github.io/scikit-learn-mooc/python_scripts/03_categorical_pipeline.html#working-with-categorical-variables
[image: image]
<https://user-images.githubusercontent.com/1680079/99403694-0ae2e680-28eb-11eb-8e5a-35884bd6bc6e.png>
I am wondering whether it is better to do in all notebooks or only in one
of the beginning at the beginning.
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Guillaume Lemaitre
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I left the one in the data exploration notebook because at this stage we don't want to introduce
make_column_selector.