.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/model_evaluation/plot_estimator_report.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_model_evaluation_plot_estimator_report.py: .. _example_estimator_report: =============================================================== `EstimatorReport`: Get insights from any scikit-learn estimator =============================================================== This example shows how the :class:`skore.EstimatorReport` class can be used to quickly get insights from any scikit-learn estimator. .. GENERATED FROM PYTHON SOURCE LINES 13-19 Loading our dataset and defining our estimator ============================================== First, we load a dataset from skrub. Our goal is to predict if a healthcare manufacturing companies paid a medical doctors or hospitals, in order to detect potential conflict of interest. .. GENERATED FROM PYTHON SOURCE LINES 21-27 .. code-block:: Python from skrub.datasets import fetch_open_payments dataset = fetch_open_payments() df = dataset.X y = dataset.y .. rst-class:: sphx-glr-script-out .. code-block:: none Downloading 'open_payments' from https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/open_payments.zip (attempt 1/3) .. GENERATED FROM PYTHON SOURCE LINES 28-32 .. code-block:: Python from skrub import TableReport TableReport(df) .. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 33-35 .. code-block:: Python TableReport(y.to_frame()) .. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 36-43 Looking at the distributions of the target, we observe that this classification task is quite imbalanced. It means that we have to be careful when selecting a set of statistical metrics to evaluate the classification performance of our predictive model. In addition, we see that the class labels are not specified by an integer 0 or 1 but instead by a string "allowed" or "disallowed". For our application, the label of interest is "allowed". .. GENERATED FROM PYTHON SOURCE LINES 43-45 .. code-block:: Python pos_label, neg_label = "allowed", "disallowed" .. GENERATED FROM PYTHON SOURCE LINES 46-48 Before training a predictive model, we need to split our dataset into a training and a validation set. .. GENERATED FROM PYTHON SOURCE LINES 48-54 .. code-block:: Python from skore import train_test_split # If you have many dataframes to split on, you can always ask train_test_split to return # a dictionary. Remember, it needs to be passed as a keyword argument! split_data = train_test_split(X=df, y=y, random_state=42, as_dict=True) .. rst-class:: sphx-glr-script-out .. code-block:: none ╭───────────────────────────── HighClassImbalanceWarning ──────────────────────────────╮ │ It seems that you have a classification problem with a high class imbalance. In this │ │ case, using train_test_split may not be a good idea because of high variability in │ │ the scores obtained on the test set. To tackle this challenge we suggest to use │ │ skore's CrossValidationReport with the `splitter` parameter of your choice. │ ╰──────────────────────────────────────────────────────────────────────────────────────╯ ╭───────────────────────────────── ShuffleTrueWarning ─────────────────────────────────╮ │ We detected that the `shuffle` parameter is set to `True` either explicitly or from │ │ its default value. In case of time-ordered events (even if they are independent), │ │ this will result in inflated model performance evaluation because natural drift will │ │ not be taken into account. We recommend setting the shuffle parameter to `False` in │ │ order to ensure the evaluation process is really representative of your production │ │ release process. │ ╰──────────────────────────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 55-65 By the way, notice how skore's :func:`~skore.train_test_split` automatically warns us for a class imbalance. Now, we need to define a predictive model. Hopefully, `skrub` provides a convenient function (:func:`skrub.tabular_pipeline`) when it comes to getting strong baseline predictive models with a single line of code. As its feature engineering is generic, it does not provide some handcrafted and tailored feature engineering but still provides a good starting point. So let's create a classifier for our task. .. GENERATED FROM PYTHON SOURCE LINES 65-70 .. code-block:: Python from skrub import tabular_pipeline estimator = tabular_pipeline("classifier") estimator .. raw:: html 
Pipeline(steps=[('tablevectorizer',
                     TableVectorizer(low_cardinality=ToCategorical())),
                    ('histgradientboostingclassifier',
                     HistGradientBoostingClassifier())])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 71-80 Getting insights from our estimator =================================== Introducing the :class:`skore.EstimatorReport` class ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Now, we would be interested in getting some insights from our predictive model. One way is to use the :class:`skore.EstimatorReport` class. This constructor will detect that our estimator is unfitted and will fit it for us on the training data. .. GENERATED FROM PYTHON SOURCE LINES 80-84 .. code-block:: Python from skore import EstimatorReport report = EstimatorReport(estimator, **split_data, pos_label=pos_label) .. GENERATED FROM PYTHON SOURCE LINES 85-88 Once the report is created, we get some information regarding the available tools allowing us to get some insights from our specific model on our specific task by calling the :meth:`~skore.EstimatorReport.help` method. .. GENERATED FROM PYTHON SOURCE LINES 89-91 .. code-block:: Python report.help() .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 92-93 Be aware that we can access the help for each individual sub-accessor. For instance: .. GENERATED FROM PYTHON SOURCE LINES 94-96 .. code-block:: Python report.metrics.help() .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 97-105 Metrics computation with aggressive caching ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ At this point, we might be interested to have a first look at the statistical performance of our model on the validation set that we provided. We can access it by calling any of the metrics displayed above. Since we are greedy, we want to get several metrics at once and we will use the :meth:`~skore.EstimatorReport.metrics.summarize` method. .. GENERATED FROM PYTHON SOURCE LINES 106-113 .. code-block:: Python import time start = time.time() metric_report = report.metrics.summarize().frame() end = time.time() metric_report .. raw:: html
HistGradientBoostingClassifier
Metric
Accuracy 0.951550
Precision 0.675225
Recall 0.451890
ROC AUC 0.057864
Brier score 0.891698
Fit time (s) 9.552743
Predict time (s) 1.520260


.. GENERATED FROM PYTHON SOURCE LINES 114-116 .. code-block:: Python print(f"Time taken to compute the metrics: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the metrics: 3.24 seconds .. GENERATED FROM PYTHON SOURCE LINES 117-124 An interesting feature provided by the :class:`skore.EstimatorReport` is the the caching mechanism. Indeed, when we have a large enough dataset, computing the predictions for a model is not cheap anymore. For instance, on our smallish dataset, it took a couple of seconds to compute the metrics. The report will cache the predictions and if we are interested in computing a metric again or an alternative metric that requires the same predictions, it will be faster. Let's check by requesting the same metrics report again. .. GENERATED FROM PYTHON SOURCE LINES 125-131 .. code-block:: Python start = time.time() metric_report = report.metrics.summarize().frame() end = time.time() metric_report .. raw:: html
HistGradientBoostingClassifier
Metric
Accuracy 0.951550
Precision 0.675225
Recall 0.451890
ROC AUC 0.057864
Brier score 0.891698
Fit time (s) 9.552743
Predict time (s) 1.520260


.. GENERATED FROM PYTHON SOURCE LINES 132-134 .. code-block:: Python print(f"Time taken to compute the metrics: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the metrics: 0.00 seconds .. GENERATED FROM PYTHON SOURCE LINES 135-137 Note that when the model is fitted or the predictions are computed, we additionally store the time the operation took: .. GENERATED FROM PYTHON SOURCE LINES 138-140 .. code-block:: Python report.metrics.timings() .. rst-class:: sphx-glr-script-out .. code-block:: none {'fit_time': 9.552743284000002, 'predict_time_test': 1.5202597630000128} .. GENERATED FROM PYTHON SOURCE LINES 141-143 Since we obtain a pandas dataframe, we can also use the plotting interface of pandas. .. GENERATED FROM PYTHON SOURCE LINES 144-149 .. code-block:: Python import matplotlib.pyplot as plt ax = metric_report.plot.barh() ax.set_title("Metrics report") .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_001.png :alt: Metrics report :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Text(0.5, 1.0, 'Metrics report') .. GENERATED FROM PYTHON SOURCE LINES 150-152 Whenever computing a metric, we check if the predictions are available in the cache and reload them if available. So for instance, let's compute the log loss. .. GENERATED FROM PYTHON SOURCE LINES 153-159 .. code-block:: Python start = time.time() log_loss = report.metrics.log_loss() end = time.time() log_loss .. rst-class:: sphx-glr-script-out .. code-block:: none 4.2063564193295875 .. GENERATED FROM PYTHON SOURCE LINES 160-162 .. code-block:: Python print(f"Time taken to compute the log loss: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the log loss: 0.03 seconds .. GENERATED FROM PYTHON SOURCE LINES 163-165 We can show that without initial cache, it would have taken more time to compute the log loss. .. GENERATED FROM PYTHON SOURCE LINES 166-173 .. code-block:: Python report.clear_cache() start = time.time() log_loss = report.metrics.log_loss() end = time.time() log_loss .. rst-class:: sphx-glr-script-out .. code-block:: none 4.2063564193295875 .. GENERATED FROM PYTHON SOURCE LINES 174-176 .. code-block:: Python print(f"Time taken to compute the log loss: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the log loss: 1.56 seconds .. GENERATED FROM PYTHON SOURCE LINES 177-180 By default, the metrics are computed on the test set only. However, if a training set is provided, we can also compute the metrics by specifying the `data_source` parameter. .. GENERATED FROM PYTHON SOURCE LINES 181-183 .. code-block:: Python report.metrics.log_loss(data_source="train") .. rst-class:: sphx-glr-script-out .. code-block:: none 4.2417141982153 .. GENERATED FROM PYTHON SOURCE LINES 184-188 Be aware that we can also benefit from the caching mechanism with our own custom metrics. Skore only expects that we define our own metric function to take `y_true` and `y_pred` as the first two positional arguments. It can take any other arguments. Let's see an example. .. GENERATED FROM PYTHON SOURCE LINES 189-203 .. code-block:: Python def operational_decision_cost(y_true, y_pred, amount): mask_true_positive = (y_true == pos_label) & (y_pred == pos_label) mask_true_negative = (y_true == neg_label) & (y_pred == neg_label) mask_false_positive = (y_true == neg_label) & (y_pred == pos_label) mask_false_negative = (y_true == pos_label) & (y_pred == neg_label) fraudulent_refuse = mask_true_positive.sum() * 50 fraudulent_accept = -amount[mask_false_negative].sum() legitimate_refuse = mask_false_positive.sum() * -5 legitimate_accept = (amount[mask_true_negative] * 0.02).sum() return fraudulent_refuse + fraudulent_accept + legitimate_refuse + legitimate_accept .. GENERATED FROM PYTHON SOURCE LINES 204-208 In our use case, we have a operational decision to make that translate the classification outcome into a cost. It translate the confusion matrix into a cost matrix based on some amount linked to each sample in the dataset that are provided to us. Here, we randomly generate some amount as an illustration. .. GENERATED FROM PYTHON SOURCE LINES 209-214 .. code-block:: Python import numpy as np rng = np.random.default_rng(42) amount = rng.integers(low=100, high=1000, size=len(split_data["y_test"])) .. GENERATED FROM PYTHON SOURCE LINES 215-217 Let's make sure that a function called the `predict` method and cached the result. We compute the accuracy metric to make sure that the `predict` method is called. .. GENERATED FROM PYTHON SOURCE LINES 218-220 .. code-block:: Python report.metrics.accuracy() .. rst-class:: sphx-glr-script-out .. code-block:: none 0.9515497553017944 .. GENERATED FROM PYTHON SOURCE LINES 221-222 We can now compute the cost of our operational decision. .. GENERATED FROM PYTHON SOURCE LINES 223-230 .. code-block:: Python start = time.time() cost = report.metrics.custom_metric( metric_function=operational_decision_cost, response_method="predict", amount=amount ) end = time.time() cost .. rst-class:: sphx-glr-script-out .. code-block:: none -138416.38 .. GENERATED FROM PYTHON SOURCE LINES 231-233 .. code-block:: Python print(f"Time taken to compute the cost: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the cost: 0.01 seconds .. GENERATED FROM PYTHON SOURCE LINES 234-235 Let's now clean the cache and see if it is faster. .. GENERATED FROM PYTHON SOURCE LINES 236-238 .. code-block:: Python report.clear_cache() .. GENERATED FROM PYTHON SOURCE LINES 239-246 .. code-block:: Python start = time.time() cost = report.metrics.custom_metric( metric_function=operational_decision_cost, response_method="predict", amount=amount ) end = time.time() cost .. rst-class:: sphx-glr-script-out .. code-block:: none -138416.38 .. GENERATED FROM PYTHON SOURCE LINES 247-249 .. code-block:: Python print(f"Time taken to compute the cost: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the cost: 1.53 seconds .. GENERATED FROM PYTHON SOURCE LINES 250-253 We observe that caching is working as expected. It is really handy because it means that we can compute some additional metrics without having to recompute the the predictions. .. GENERATED FROM PYTHON SOURCE LINES 254-263 .. code-block:: Python report.metrics.summarize( metric={ "Precision": "precision", "Recall": "recall", "Operational Decision Cost": operational_decision_cost, }, metric_kwargs={"amount": amount, "response_method": "predict"}, ).frame() .. raw:: html
HistGradientBoostingClassifier
Metric
Precision 0.675225
Recall 0.451890
Operational Decision Cost -138416.380000


.. GENERATED FROM PYTHON SOURCE LINES 264-268 It could happen that we are interested in providing several custom metrics which does not necessarily share the same parameters. In this more complex case, skore will require us to provide a scorer using the :func:`sklearn.metrics.make_scorer` function. .. GENERATED FROM PYTHON SOURCE LINES 269-282 .. code-block:: Python from sklearn.metrics import f1_score, make_scorer f1_scorer = make_scorer(f1_score, response_method="predict") operational_decision_cost_scorer = make_scorer( operational_decision_cost, response_method="predict", amount=amount ) report.metrics.summarize( metric={ "F1 Score": f1_scorer, "Operational Decision Cost": operational_decision_cost_scorer, }, ).frame() .. raw:: html
HistGradientBoostingClassifier
Metric
F1 Score 0.541431
Operational Decision Cost -138416.380000


.. GENERATED FROM PYTHON SOURCE LINES 283-289 Effortless one-liner plotting ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The :class:`skore.EstimatorReport` class also provides a plotting interface that allows to plot *defacto* the most common plots. As for the metrics, we only provide the meaningful set of plots for the provided estimator. .. GENERATED FROM PYTHON SOURCE LINES 290-292 .. code-block:: Python report.metrics.help() .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 293-294 Let's start by plotting the ROC curve for our binary classification task. .. GENERATED FROM PYTHON SOURCE LINES 295-298 .. code-block:: Python display = report.metrics.roc() display.plot() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_002.png :alt: ROC Curve for HistGradientBoostingClassifier Positive label: allowed Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 299-303 The plot functionality is built upon the scikit-learn display objects. We return those display (slightly modified to improve the UI) in case we want to tweak some of the plot properties. We can have quick look at the available attributes and methods by calling the ``help`` method or simply by printing the display. .. GENERATED FROM PYTHON SOURCE LINES 304-306 .. code-block:: Python display .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 307-309 .. code-block:: Python display.help() .. raw:: html


.. GENERATED FROM PYTHON SOURCE LINES 310-313 .. code-block:: Python display.plot() _ = display.ax_.set_title("Example of a ROC curve") .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_003.png :alt: ROC Curve for HistGradientBoostingClassifier Positive label: allowed Data source: Test set, Example of a ROC curve :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 314-318 Similarly to the metrics, we aggressively use the caching to avoid recomputing the predictions of the model. We also cache the plot display object by detection if the input parameters are the same as the previous call. Let's demonstrate the kind of performance gain we can get. .. GENERATED FROM PYTHON SOURCE LINES 319-325 .. code-block:: Python start = time.time() # we already trigger the computation of the predictions in a previous call display = report.metrics.roc() display.plot() end = time.time() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_004.png :alt: ROC Curve for HistGradientBoostingClassifier Positive label: allowed Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 326-328 .. code-block:: Python print(f"Time taken to compute the ROC curve: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the ROC curve: 0.12 seconds .. GENERATED FROM PYTHON SOURCE LINES 329-330 Now, let's clean the cache and check if we get a slowdown. .. GENERATED FROM PYTHON SOURCE LINES 331-333 .. code-block:: Python report.clear_cache() .. GENERATED FROM PYTHON SOURCE LINES 334-339 .. code-block:: Python start = time.time() display = report.metrics.roc() display.plot() end = time.time() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_005.png :alt: ROC Curve for HistGradientBoostingClassifier Positive label: allowed Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 340-342 .. code-block:: Python print(f"Time taken to compute the ROC curve: {end - start:.2f} seconds") .. rst-class:: sphx-glr-script-out .. code-block:: none Time taken to compute the ROC curve: 1.67 seconds .. GENERATED FROM PYTHON SOURCE LINES 343-344 As expected, since we need to recompute the predictions, it takes more time. .. GENERATED FROM PYTHON SOURCE LINES 346-351 Visualizing the confusion matrix ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Another useful visualization for classification tasks is the confusion matrix, which shows the counts of correct and incorrect predictions for each class. .. GENERATED FROM PYTHON SOURCE LINES 353-354 Let's first start with a basic confusion matrix: .. GENERATED FROM PYTHON SOURCE LINES 354-358 .. code-block:: Python cm_display = report.metrics.confusion_matrix() cm_display.plot() plt.show(block=True) .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_006.png :alt: Confusion Matrix Decision threshold: 0.50 Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_006.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 359-363 In binary classification, a confusion matrix depends on the decision threshold used to convert predicted probabilities into class labels. By default, skore uses a threshold of 0.5, but confusion matrices are actually computed at every threshold internally. You can access all available thresholds via the ``thresholds`` attribute: .. GENERATED FROM PYTHON SOURCE LINES 363-368 .. code-block:: Python print(f"Number of thresholds: {len(cm_display.thresholds)}") print( f"Threshold range: [{cm_display.thresholds.min():.3f}, {cm_display.thresholds.max():.3f}]" ) .. rst-class:: sphx-glr-script-out .. code-block:: none Number of thresholds: 17999 Threshold range: [0.000, 0.939] .. GENERATED FROM PYTHON SOURCE LINES 369-371 To visualize the confusion matrix at a different threshold, use the ``threshold_value`` parameter. For example, a threshold of 0.3 will classify more samples as positive: .. GENERATED FROM PYTHON SOURCE LINES 371-374 .. code-block:: Python cm_display.plot(threshold_value=0.3) plt.show(block=True) .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_007.png :alt: Confusion Matrix Decision threshold: 0.30 Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_007.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 375-377 We can normalize the confusion matrix to get percentages instead of raw counts. Here we normalize by true labels (rows): .. GENERATED FROM PYTHON SOURCE LINES 377-380 .. code-block:: Python cm_display.plot(normalize="true") plt.show(block=True) .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_008.png :alt: Confusion Matrix Decision threshold: 0.50 Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_008.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 381-383 More plotting options are available via ``heatmap_kwargs``, which are passed to seaborn's heatmap. For example, we can customize the colormap and number format: .. GENERATED FROM PYTHON SOURCE LINES 383-387 .. code-block:: Python cm_display.set_style(heatmap_kwargs={"cmap": "Greens", "fmt": ".2e"}) cm_display.plot() plt.show(block=True) .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_009.png :alt: Confusion Matrix Decision threshold: 0.50 Data source: Test set :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_009.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 388-390 Finally, the confusion matrix can also be exported as a pandas DataFrame for further analysis: .. GENERATED FROM PYTHON SOURCE LINES 390-393 .. code-block:: Python cm_frame = cm_display.frame() cm_frame .. raw:: html
true_label predicted_label value threshold split estimator data_source
2840 disallowed disallowed 16972 0.499929 None HistGradientBoostingClassifier test
2841 disallowed allowed 254 0.499929 None HistGradientBoostingClassifier test
2842 allowed disallowed 638 0.499929 None HistGradientBoostingClassifier test
2843 allowed allowed 526 0.499929 None HistGradientBoostingClassifier test


.. GENERATED FROM PYTHON SOURCE LINES 394-398 .. seealso:: For using the :class:`~skore.EstimatorReport` to inspect your models, see :ref:`example_feature_importance`. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 30.385 seconds) .. _sphx_glr_download_auto_examples_model_evaluation_plot_estimator_report.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_estimator_report.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_estimator_report.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_estimator_report.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_