Comments (3)
I guess this makes sense. Open to PRs
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@reiinakano I have changed plot_cumulative_gain for choosing classes for ploting also with 'micro' and 'macro' as default ploting curve. You can check it out.
from scikit-plot.
Because pytest of unit test for plot_cumulative_plot is not allowed for multi-classes plot originally, so this may cause pytest can't pass.
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Related Issues (20)
- Is this project still maintained? HOT 1
- add more argument pass through from plot_cumulative_gain to matplotlib
- Add numerical digit precision parameter HOT 1
- The True label displays abnormal HOT 2
- Add a Silhouette coefficient plot for `clusterer` module HOT 2
- Plot confusion matrix not from predictions but from actual confusion matrix HOT 1
- plot feature importances: how to get the feature names out of a pipeline?
- plot_pca_2d_projection, order of classes/labels is not preserved
- Adding class_names option to gain and lift plots
- Actively Developed? HOT 2
- add class prediction error plot
- Regarding the scikit-plot.metrics.plot_roc function HOT 1
- [BUG] Build status badge not rendering correctly in readme
- Bug: Failing to import Scikitplot fails due to ImportError HOT 4
- estimators.plot_feature_importances x_tick_rotation is misaligned
- MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated
- Too many indices For Array HOT 4
- ValueError: Found input variables with inconsistent numbers of samples HOT 3
- Is there a [problem in precision_recall_curve? HOT 1
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from scikit-plot.