MAINT Use class_of_interest in DecisionBoundaryDisplay #772
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glemaitre
reviewed
Apr 26, 2024
|
I see an occurrence where we could reuse the axis from the first display, it seems more natural. import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from sklearn.inspection import DecisionBoundaryDisplay
tab10_norm = mpl.colors.Normalize(vmin=-0.5, vmax=8.5)
palette = ["tab:blue", "tab:green", "tab:orange"]
disp = DecisionBoundaryDisplay.from_estimator(
tree,
data_train,
response_method="predict",
cmap="tab10",
norm=tab10_norm,
alpha=0.5,
)
sns.scatterplot(
data=penguins,
x=culmen_columns[0],
y=culmen_columns[1],
hue=target_column,
palette=palette,
ax=disp.ax_,
)
disp.ax_.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
_ = disp.ax_.set_title("Decision boundary using a decision tree") |
glemaitre
reviewed
Apr 2, 2025
|
If this is already there in 1.4, we can directly target |
According to #789, we are currently running on v1.3. I confirmed this by running |
ogrisel
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Apr 3, 2025
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ogrisel
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May 27, 2025
* Feature branch to update to 1.6 * MNT Fix several FutureWarnings (#810) * MTN Wrap up quiz sklearn 1.6 verification (#817) * MAINT Use class_of_interest in DecisionBoundaryDisplay (#772) * Resync everything --------- Co-authored-by: Arturo Amor <[email protected]> Co-authored-by: SebastienMelo <[email protected]>
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May 27, 2025
* Feature branch to update to 1.6 * MNT Fix several FutureWarnings (#810) * MTN Wrap up quiz sklearn 1.6 verification (#817) * MAINT Use class_of_interest in DecisionBoundaryDisplay (#772) * Resync everything --------- Co-authored-by: Arturo Amor <[email protected]> Co-authored-by: SebastienMelo <[email protected]> c9728c8
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Since scikit-learn v1.4
DecisionBoundaryDisplayacceptsclass_of_interestfor multiclass visualization. This feature is promised in the current version of the MOOC.Notice that, as it requires updating the minimal version, it may change the experience of current enrolled participants.