Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

DOC add displays in highlights #25042

Merged
merged 2 commits into from Nov 25, 2022
Merged

DOC add displays in highlights #25042

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
c992ca1
DOC add displays in highlights
glemaitre Nov 25, 2022
c7c8dda
reviews
glemaitre Nov 25, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
25 changes: 25 additions & 0 deletions examples/release_highlights/plot_release_highlights_1_2_0.py
View file
Open in desktop
Expand Up @@ -68,6 +68,31 @@
)
hist_no_interact.fit(X, y)

# %%
# New and enhanced displays
# -------------------------
# :class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression
# models in a qualitative manner.
import matplotlib.pyplot as plt
from sklearn.metrics import PredictionErrorDisplay

fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
PredictionErrorDisplay.from_estimator(
hist_no_interact, X, y, kind="actual_vs_predicted", ax=axs[0]
)
PredictionErrorDisplay.from_estimator(
hist_no_interact, X, y, kind="residual_vs_predicted", ax=axs[1]
)

# %%
# :class:`~model_selection.LearningCurveDisplay` is now available to plot
# results from :func:`~model_selection.learning_curve`.
from sklearn.model_selection import LearningCurveDisplay

LearningCurveDisplay.from_estimator(
hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
)

# %%
# Faster parser in :func:`~datasets.fetch_openml`
# -----------------------------------------------
Expand Down