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Some background here: SVC does not really scale with the number of samples. It was useful at the time that kernels were popular. But nowadays, I would even advocate with a kernel approximation and a logistic regression to achieve the same thing and it will scale.
So as a general rule, I think that we should really either show:
LogisticRegression when it comes to classification with linear model
HistGradientBoosting in regression and classification when we want to show the direct state of the art models.
Then, we can use any other classifier or regressor but it means that we want to show a specific feature of this particular model.
Which part of the documentation needs improvement?
README, quick start example
Describe the problem found in the documentation
Currently, on the Iris dataset, for classification, we use SVC.
Suggested improvement
@glemaitre suggests using logistic regression.
Additional context
Related to #1004.
Waiting on this PR to be merged: #1009
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