scikit-learn
diff --git a/Diff for: ‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
-109 Bytes b/Diff for: ‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
-109 Bytes
diff --git a/Diff for: ‎dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb
+4-4 b/Diff for: ‎dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb
+4-4
diff --git a/Diff for: ‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
-125 Bytes b/Diff for: ‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
-125 Bytes
diff --git a/Diff for: ‎dev/_downloads/bcd609cfe29c9da1f51c848e18b89c76/plot_partial_dependence.py
+15-23 b/Diff for: ‎dev/_downloads/bcd609cfe29c9da1f51c848e18b89c76/plot_partial_dependence.py
+15-23
diff --git a/Diff for: ‎dev/_downloads/scikit-learn-docs.zip
50.1 KB b/Diff for: ‎dev/_downloads/scikit-learn-docs.zip
50.1 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
-40 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
-40 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
209 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
209 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
151 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
151 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
-13 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
-13 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_anomaly_comparison_001.png
-384 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_anomaly_comparison_001.png
-384 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_anomaly_comparison_thumb.png
-11 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_anomaly_comparison_thumb.png
-11 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_caching_nearest_neighbors_001.png
3.45 KB b/Diff for: ‎dev/_images/sphx_glr_plot_caching_nearest_neighbors_001.png
3.45 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png
1.51 KB b/Diff for: ‎dev/_images/sphx_glr_plot_caching_nearest_neighbors_thumb.png
1.51 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
1.06 KB b/Diff for: ‎dev/_images/sphx_glr_plot_cluster_comparison_001.png
1.06 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_cluster_comparison_thumb.png
-1 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_cluster_comparison_thumb.png
-1 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_001.png
140 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_001.png
140 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_002.png
-75 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_002.png
-75 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_003.png
-42 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_003.png
-42 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_thumb.png
53 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_coin_segmentation_thumb.png
53 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_compare_calibration_001.png
-64 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_compare_calibration_001.png
-64 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_compare_calibration_thumb.png
63 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_compare_calibration_thumb.png
63 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_compare_methods_001.png
-359 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_compare_methods_001.png
-359 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_compare_methods_thumb.png
73 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_compare_methods_thumb.png
73 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_dict_face_patches_001.png
94 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_dict_face_patches_001.png
94 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_dict_face_patches_thumb.png
108 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_dict_face_patches_thumb.png
108 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_digits_pipe_001.png
-77 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_digits_pipe_001.png
-77 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_digits_pipe_thumb.png
-54 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_digits_pipe_thumb.png
-54 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_document_classification_20newsgroups_001.png
12 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_document_classification_20newsgroups_001.png
12 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_document_classification_20newsgroups_thumb.png
-2 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_document_classification_20newsgroups_thumb.png
-2 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_faces_decomposition_004.png
56 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_faces_decomposition_004.png
56 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_faces_decomposition_007.png
-94 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_faces_decomposition_007.png
-94 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_categorical_001.png
-2.51 KB b/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_categorical_001.png
-2.51 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_categorical_002.png
1.04 KB b/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_categorical_002.png
1.04 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_categorical_thumb.png
-1022 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_categorical_thumb.png
-1022 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_early_stopping_002.png
277 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_gradient_boosting_early_stopping_002.png
277 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_001.png
85 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_001.png
85 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_003.png
-157 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_003.png
-157 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_005.png
-1 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_005.png
-1 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_thumb.png
56 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_image_denoising_thumb.png
56 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_kernel_approximation_001.png
-169 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_kernel_approximation_001.png
-169 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_kernel_approximation_thumb.png
182 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_kernel_approximation_thumb.png
182 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_kernel_ridge_regression_001.png
20 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_kernel_ridge_regression_001.png
20 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_kernel_ridge_regression_002.png
1 KB b/Diff for: ‎dev/_images/sphx_glr_plot_kernel_ridge_regression_002.png
1 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_kernel_ridge_regression_thumb.png
25 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_kernel_ridge_regression_thumb.png
25 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lasso_model_selection_003.png
89 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lasso_model_selection_003.png
89 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_learning_curve_001.png
-1.63 KB b/Diff for: ‎dev/_images/sphx_glr_plot_learning_curve_001.png
-1.63 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_learning_curve_thumb.png
-117 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_learning_curve_thumb.png
-117 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_linear_model_coefficient_interpretation_001.png
-69 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_linear_model_coefficient_interpretation_001.png
-69 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png
-38 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_linear_model_coefficient_interpretation_thumb.png
-38 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_linkage_comparison_001.png
-110 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_linkage_comparison_001.png
-110 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_linkage_comparison_thumb.png
3 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_linkage_comparison_thumb.png
3 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_004.png
-71 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_004.png
-71 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_005.png
23 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_005.png
23 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_006.png
13 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_006.png
13 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_007.png
-24 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_007.png
-24 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_008.png
-34 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_008.png
-34 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_009.png
-11 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_009.png
-11 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_010.png
-66 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_010.png
-66 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_011.png
-46 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_011.png
-46 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_012.png
93 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_012.png
93 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_013.png
68 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_013.png
68 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_014.png
41 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_lle_digits_014.png
41 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_manifold_sphere_001.png
84 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_manifold_sphere_001.png
84 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_manifold_sphere_thumb.png
147 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_manifold_sphere_thumb.png
147 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_001.png
564 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_001.png
564 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_002.png
-226 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_002.png
-226 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_003.png
-110 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_003.png
-110 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_thumb.png
362 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_model_complexity_influence_thumb.png
362 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_out_of_core_classification_002.png
455 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_out_of_core_classification_002.png
455 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_out_of_core_classification_003.png
-556 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_out_of_core_classification_003.png
-556 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_out_of_core_classification_004.png
44 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_out_of_core_classification_004.png
44 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_001.png
5.06 KB b/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_001.png
5.06 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_002.png
4.56 KB b/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_002.png
4.56 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_003.png
38.5 KB b/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_003.png
38.5 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_thumb.png
2.3 KB b/Diff for: ‎dev/_images/sphx_glr_plot_partial_dependence_thumb.png
2.3 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_001.png
-625 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_001.png
-625 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_002.png
22 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_002.png
22 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_003.png
-1.93 KB b/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_003.png
-1.93 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_004.png
15 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_004.png
15 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_thumb.png
-403 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_prediction_latency_thumb.png
-403 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_release_highlights_0_24_0_001.png
-223 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_release_highlights_0_24_0_001.png
-223 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png
-443 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_release_highlights_0_24_0_thumb.png
-443 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_scalable_poly_kernels_001.png
144 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_scalable_poly_kernels_001.png
144 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_scalable_poly_kernels_thumb.png
209 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_scalable_poly_kernels_thumb.png
209 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_sgd_early_stopping_001.png
-6.24 KB b/Diff for: ‎dev/_images/sphx_glr_plot_sgd_early_stopping_001.png
-6.24 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_sgd_early_stopping_thumb.png
-1.8 KB b/Diff for: ‎dev/_images/sphx_glr_plot_sgd_early_stopping_thumb.png
-1.8 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_001.png
1.7 KB b/Diff for: ‎dev/_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_001.png
1.7 KB
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png
191 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png
191 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_stack_predictors_001.png
-359 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_stack_predictors_001.png
-359 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_stack_predictors_thumb.png
-11 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_stack_predictors_thumb.png
-11 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_successive_halving_heatmap_001.png
863 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_successive_halving_heatmap_001.png
863 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_successive_halving_heatmap_thumb.png
191 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_successive_halving_heatmap_thumb.png
191 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_theilsen_001.png
-17 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_theilsen_001.png
-17 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_theilsen_002.png
2 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_theilsen_002.png
2 Bytes
diff --git a/Diff for: ‎dev/_images/sphx_glr_plot_theilsen_thumb.png
2 Bytes b/Diff for: ‎dev/_images/sphx_glr_plot_theilsen_thumb.png
2 Bytes
diff --git a/Diff for: ‎dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt
+1-1 b/Diff for: ‎dev/_sources/auto_examples/applications/plot_cyclical_feature_engineering.rst.txt
+1-1
diff --git a/Diff for: ‎dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt
+1-1 b/Diff for: ‎dev/_sources/auto_examples/applications/plot_digits_denoising.rst.txt
+1-1
diff --git a/Diff for: ‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
+5-5 b/Diff for: ‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
+5-5
diff --git a/Diff for: ‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
+15-15 b/Diff for: ‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
+15-15
@@ -69,7 +69,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nprint(\"Computing partial dependence plots...\")\ntic = time()\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features,\n    kind=\"both\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n    ice_lines_kw={\"color\": \"tab:blue\", \"alpha\": 0.2, \"linewidth\": 0.5},\n    pd_line_kw={\"color\": \"tab:orange\", \"linestyle\": \"--\"},\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with MLPRegressor\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
+        "from sklearn.inspection import PartialDependenceDisplay\n\ncommon_params = {\"subsample\": 50, \"n_jobs\": 2, \"grid_resolution\": 20, \"random_state\": 0}\n\nprint(\"Computing partial dependence plots...\")\ntic = time()\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features=[\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"],\n    kind=\"both\",\n    **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with MLPRegressor\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "print(\"Computing partial dependence plots...\")\ntic = time()\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features,\n    kind=\"both\",\n    subsample=50,\n    n_jobs=3,\n    grid_resolution=20,\n    random_state=0,\n    ice_lines_kw={\"color\": \"tab:blue\", \"alpha\": 0.2, \"linewidth\": 0.5},\n    pd_line_kw={\"color\": \"tab:orange\", \"linestyle\": \"--\"},\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
+        "print(\"Computing partial dependence plots...\")\ntic = time()\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features=[\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"],\n    kind=\"both\",\n    **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
       ]
     },
     {
@@ -130,7 +130,7 @@
       },
       "outputs": [],
       "source": [
-        "features = [\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")]\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features,\n    kind=\"average\",\n    n_jobs=2,\n    grid_resolution=10,\n    ax=ax,\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
+        "import matplotlib.pyplot as plt\n\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\n\n# Note that we could have called the method `from_estimator` three times and\n# provide one feature, one kind of plot, and one axis for each call.\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features=[\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")],\n    kind=[\"both\", \"both\", \"average\"],\n    ax=ax,\n    **common_params,\n)\n\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    \"Partial dependence of house value on non-location features\\n\"\n    \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
       ]
     },
     {
@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n    est, X_train, features=features, kind=\"average\", grid_resolution=10\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\n\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n    \"Partial dependence of house value on median\\n\"\n    \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
+        "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.inspection import partial_dependence\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n    est, X_train, features=features, kind=\"average\", grid_resolution=10\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\n\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n    \"Partial dependence of house value on median\\n\"\n    \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
       ]
     }
   ],
 
@@ -111,24 +111,18 @@
 # We will plot the partial dependence, both individual (ICE) and averaged one
 # (PDP). We limit to only 50 ICE curves to not overcrowd the plot.
 
-import matplotlib.pyplot as plt
-from sklearn.inspection import partial_dependence
 from sklearn.inspection import PartialDependenceDisplay
 
+common_params = {"subsample": 50, "n_jobs": 2, "grid_resolution": 20, "random_state": 0}
+
 print("Computing partial dependence plots...")
 tic = time()
-features = ["MedInc", "AveOccup", "HouseAge", "AveRooms"]
 display = PartialDependenceDisplay.from_estimator(
     est,
     X_train,
-    features,
+    features=["MedInc", "AveOccup", "HouseAge", "AveRooms"],
     kind="both",
-    subsample=50,
-    n_jobs=3,
-    grid_resolution=20,
-    random_state=0,
-    ice_lines_kw={"color": "tab:blue", "alpha": 0.2, "linewidth": 0.5},
-    pd_line_kw={"color": "tab:orange", "linestyle": "--"},
+    **common_params,
 )
 print(f"done in {time() - tic:.3f}s")
 display.figure_.suptitle(
@@ -171,14 +165,9 @@
 display = PartialDependenceDisplay.from_estimator(
     est,
     X_train,
-    features,
+    features=["MedInc", "AveOccup", "HouseAge", "AveRooms"],
     kind="both",
-    subsample=50,
-    n_jobs=3,
-    grid_resolution=20,
-    random_state=0,
-    ice_lines_kw={"color": "tab:blue", "alpha": 0.2, "linewidth": 0.5},
-    pd_line_kw={"color": "tab:orange", "linestyle": "--"},
+    **common_params,
 )
 print(f"done in {time() - tic:.3f}s")
 display.figure_.suptitle(
@@ -226,20 +215,23 @@
 # Another consideration is linked to the performance to compute the PDPs. With
 # the tree-based algorithm, when only PDPs are requested, they can be computed
 # on an efficient way using the `'recursion'` method.
+import matplotlib.pyplot as plt
 
-features = ["AveOccup", "HouseAge", ("AveOccup", "HouseAge")]
 print("Computing partial dependence plots...")
 tic = time()
 _, ax = plt.subplots(ncols=3, figsize=(9, 4))
+
+# Note that we could have called the method `from_estimator` three times and
+# provide one feature, one kind of plot, and one axis for each call.
 display = PartialDependenceDisplay.from_estimator(
     est,
     X_train,
-    features,
-    kind="average",
-    n_jobs=2,
-    grid_resolution=10,
+    features=["AveOccup", "HouseAge", ("AveOccup", "HouseAge")],
+    kind=["both", "both", "average"],
     ax=ax,
+    **common_params,
 )
+
 print(f"done in {time() - tic:.3f}s")
 display.figure_.suptitle(
     "Partial dependence of house value on non-location features\n"
@@ -260,9 +252,9 @@
 #
 # Let's make the same partial dependence plot for the 2 features interaction,
 # this time in 3 dimensions.
-
 import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
+from sklearn.inspection import partial_dependence
 
 fig = plt.figure()
Original file line number	Diff line number	Diff line change
`@@ -69,7 +69,7 @@`
`69`	`69`	`},`
`70`	`70`	`"outputs": [],`
`71`	`71`	`"source": [`
`72`		- "import matplotlib.pyplot as plt\nfrom sklearn.inspection import partial_dependence\nfrom sklearn.inspection import PartialDependenceDisplay\n\nprint(\"Computing partial dependence plots...\")\ntic = time()\nfeatures = [\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"]\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features,\n kind=\"both\",\n subsample=50,\n n_jobs=3,\n grid_resolution=20,\n random_state=0,\n ice_lines_kw={\"color\": \"tab:blue\", \"alpha\": 0.2, \"linewidth\": 0.5},\n pd_line_kw={\"color\": \"tab:orange\", \"linestyle\": \"--\"},\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n \"Partial dependence of house value on non-location features\\n\"\n \"for the California housing dataset, with MLPRegressor\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
	`72`	+ "from sklearn.inspection import PartialDependenceDisplay\n\ncommon_params = {\"subsample\": 50, \"n_jobs\": 2, \"grid_resolution\": 20, \"random_state\": 0}\n\nprint(\"Computing partial dependence plots...\")\ntic = time()\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features=[\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"],\n kind=\"both\",\n **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n \"Partial dependence of house value on non-location features\\n\"\n \"for the California housing dataset, with MLPRegressor\"\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
`73`	`73`	`]`
`74`	`74`	`},`
`75`	`75`	`{`
`@@ -105,7 +105,7 @@`
`105`	`105`	`},`
`106`	`106`	`"outputs": [],`
`107`	`107`	`"source": [`
`108`		- "print(\"Computing partial dependence plots...\")\ntic = time()\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features,\n kind=\"both\",\n subsample=50,\n n_jobs=3,\n grid_resolution=20,\n random_state=0,\n ice_lines_kw={\"color\": \"tab:blue\", \"alpha\": 0.2, \"linewidth\": 0.5},\n pd_line_kw={\"color\": \"tab:orange\", \"linestyle\": \"--\"},\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n \"Partial dependence of house value on non-location features\\n\"\n \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
	`108`	+ "print(\"Computing partial dependence plots...\")\ntic = time()\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features=[\"MedInc\", \"AveOccup\", \"HouseAge\", \"AveRooms\"],\n kind=\"both\",\n **common_params,\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n \"Partial dependence of house value on non-location features\\n\"\n \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
`109`	`109`	`]`
`110`	`110`	`},`
`111`	`111`	`{`
`@@ -130,7 +130,7 @@`
`130`	`130`	`},`
`131`	`131`	`"outputs": [],`
`132`	`132`	`"source": [`
`133`		- "features = [\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")]\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features,\n kind=\"average\",\n n_jobs=2,\n grid_resolution=10,\n ax=ax,\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n \"Partial dependence of house value on non-location features\\n\"\n \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
	`133`	+ "import matplotlib.pyplot as plt\n\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\n\n# Note that we could have called the method `from_estimator` three times and\n# provide one feature, one kind of plot, and one axis for each call.\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features=[\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")],\n kind=[\"both\", \"both\", \"average\"],\n ax=ax,\n **common_params,\n)\n\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n \"Partial dependence of house value on non-location features\\n\"\n \"for the California housing dataset, with Gradient Boosting\"\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
`134`	`134`	`]`
`135`	`135`	`},`
`136`	`136`	`{`
`@@ -148,7 +148,7 @@`
`148`	`148`	`},`
`149`	`149`	`"outputs": [],`
`150`	`150`	`"source": [`
`151`		- "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n est, X_train, features=features, kind=\"average\", grid_resolution=10\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\n\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n \"Partial dependence of house value on median\\n\"\n \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
	`151`	+ "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom sklearn.inspection import partial_dependence\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n est, X_train, features=features, kind=\"average\", grid_resolution=10\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\n\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n \"Partial dependence of house value on median\\n\"\n \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
`152`	`152`	`]`
`153`	`153`	`}`
`154`	`154`	`],`