DOC Rework Importance of Feature Scaling example #25012
Conversation
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Weird that the CIs did not start. I merged |
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…nto scaling_importance
Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
…arn into scaling_importance
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
…arn into scaling_importance
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I think all of your comments have been addressed, @glemaitre and @lorentzenchr. |
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I will have to check the rendering but I think that the proposal is already an improvement. |
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| scaled_X_train = scaler.fit_transform(X_train) | ||
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Optional: We could show the mean value of each feature, or min and max.
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| # The need for regularization is higher (lower values of `C`) for the data | ||
| # that was not scaled before applying PCA. From the plot we can confirm that |
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Which plot? Is it over- or underfitting?
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By plotting the validation curves I realized that the training and test accuracy overlap too much to make a proper statement about over- or underfitting for the scenario with no standardization.
I think that it is better to avoid mentioning over-/underfitting to keep the example as simple as possible.
Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
…nto scaling_importance
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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I certainly broke the linter with my suggestion. Sorry @ArturoAmorQ |
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
…nto scaling_importance
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com>
Reference Issues/PRs
Fixes #12282.
What does this implement/fix? Explain your changes.
This example can benefit from a "tutorialization". In particular, this PR adds a section regarding how nearest neighbors is sensitive to scaling.
Any other comments?
Side effect: Implements notebook style as intended in #22406.