8000 [Doc] sorting feature wrt imp by venkyyuvy · Pull Request #17331 · scikit-learn/scikit-learn · GitHub
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[Doc] sorting feature wrt imp #17331

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Merged
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May 25, 2020
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[Doc] sorting feature wrt imp #17331

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sorting feature wrt imp
venkyyuvy May 25, 2020
f1e0672
re ordering
venkyyuvy May 25, 2020
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7 changes: 6 additions & 1 deletion examples/release_highlights/plot_release_highlights_0_22_0.py
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Expand Up @@ -100,18 +100,23 @@
# The :func:`inspection.permutation_importance` can be used to get an
# estimate of the importance of each feature, for any fitted estimator:

import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from sklearn.inspection import permutation_importance

X, y = make_classification(random_state=0, n_features=5, n_informative=3)
feature_names = np.array([f'x_{i}' for i in range(X.shape[1])])

rf = RandomForestClassifier(random_state=0).fit(X, y)
result = permutation_importance(rf, X, y, n_repeats=10, random_state=0,
n_jobs=-1)

fig, ax = plt.subplots()
sorted_idx = result.importances_mean.argsort()
ax.boxplot(result.importances[sorted_idx].T,
vert=False, labels=range(X.shape[1]))
vert=False, labels=feature_names[sorted_idx])
ax.set_title("Permutation Importance of each feature")
ax.set_ylabel("Features")
fig.tight_layout()
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