.. 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: ============================================ 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 13-18 Loading our dataset and defining our estimator ============================================== First, we load a dataset from skrub. Our goal is to predict if a company paid a physician. The ultimate goal is to detect potential conflict of interest when it comes to the actual problem that we want to solve. .. GENERATED FROM PYTHON SOURCE LINES 20-26 .. code-block:: Python from skrub.datasets import fetch_open_payments dataset = fetch_open_payments() df = dataset.X y = dataset.y .. GENERATED FROM PYTHON SOURCE LINES 27-31 .. code-block:: Python from skrub import TableReport TableReport(df) .. raw:: html

Please enable javascript

The skrub table reports need javascript to display correctly. If you are displaying a report in a Jupyter notebook and you see this message, you may need to re-execute the cell or to trust the notebook (button on the top right or "File > Trust notebook").



.. GENERATED FROM PYTHON SOURCE LINES 32-34 .. code-block:: Python TableReport(y.to_frame()) .. raw:: html

Please enable javascript

The skrub table reports need javascript to display correctly. If you are displaying a report in a Jupyter notebook and you see this message, you may need to re-execute the cell or to trust the notebook (button on the top right or "File > Trust notebook").



.. GENERATED FROM PYTHON SOURCE LINES 35-42 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 42-44 .. code-block:: Python pos_label, neg_label = "allowed", "disallowed" .. GENERATED FROM PYTHON SOURCE LINES 45-47 Before training a predictive model, we need to split our dataset into a training and a validation set. .. GENERATED FROM PYTHON SOURCE LINES 47-51 .. code-block:: Python from skore import train_test_split X_train, X_test, y_train, y_test = train_test_split(df, y, random_state=42) .. 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 cross_validate function. │ ╰──────────────────────────────────────────────────────────────────────────────────────╯ ╭───────────────────────────────── 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 52-62 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_learner`) 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 and fit it on the training set. .. GENERATED FROM PYTHON SOURCE LINES 62-67 .. code-block:: Python from skrub import tabular_learner estimator = tabular_learner("classifier").fit(X_train, y_train) estimator .. raw:: html 
Pipeline(steps=[('tablevectorizer',
                     TableVectorizer(high_cardinality=MinHashEncoder(),
                                     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 68-77 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 already fitted and will not fit it again. .. GENERATED FROM PYTHON SOURCE LINES 77-84 .. code-block:: Python from skore import EstimatorReport report = EstimatorReport( estimator, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test ) report .. rst-class:: sphx-glr-script-out .. code-block:: none ╭─────── skore.EstimatorReport ────────╮ │ Get guidance using the help() method │ ╰──────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 85-90 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. We can get a similar information if we call the :meth:`~skore.EstimatorReport.help` method. .. GENERATED FROM PYTHON SOURCE LINES 91-93 .. code-block:: Python report.help() .. rst-class:: sphx-glr-script-out .. code-block:: none ╭──────────── Tools to diagnose estimator HistGradientBoostingClassifier ─────────────╮ │ report │ │ ├── .metrics │ │ │ ├── .accuracy(...) (↗︎) - Compute the accuracy score. │ │ │ ├── .brier_score(...) (↘︎) - Compute the Brier score. │ │ │ ├── .log_loss(...) (↘︎) - Compute the log loss. │ │ │ ├── .precision(...) (↗︎) - Compute the precision score. │ │ │ ├── .recall(...) (↗︎) - Compute the recall score. │ │ │ ├── .roc_auc(...) (↗︎) - Compute the ROC AUC score. │ │ │ ├── .custom_metric(...) - Compute a custom metric. │ │ │ ├── .report_metrics(...) - Report a set of metrics for our estimator. │ │ │ └── .plot │ │ │ ├── .precision_recall(...) - Plot the precision-recall curve. │ │ │ └── .roc(...) - Plot the ROC curve. │ │ ├── .cache_predictions(...) - Cache estimator's predictions. │ │ ├── .clear_cache(...) - Clear the cache. │ │ └── Attributes │ │ ├── .X_test │ │ ├── .X_train │ │ ├── .y_test │ │ ├── .y_train │ │ ├── .estimator_ │ │ └── .estimator_name_ │ │ │ │ │ │ Legend: │ │ (↗︎) higher is better (↘︎) lower is better │ ╰─────────────────────────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 94-95 Be aware that we can access the help for each individual sub-accessor. For instance: .. GENERATED FROM PYTHON SOURCE LINES 96-98 .. code-block:: Python report.metrics.help() .. rst-class:: sphx-glr-script-out .. code-block:: none ╭─────────────────────────── Available metrics methods ───────────────────────────╮ │ report.metrics │ │ ├── .accuracy(...) (↗︎) - Compute the accuracy score. │ │ ├── .brier_score(...) (↘︎) - Compute the Brier score. │ │ ├── .log_loss(...) (↘︎) - Compute the log loss. │ │ ├── .precision(...) (↗︎) - Compute the precision score. │ │ ├── .recall(...) (↗︎) - Compute the recall score. │ │ ├── .roc_auc(...) (↗︎) - Compute the ROC AUC score. │ │ ├── .custom_metric(...) - Compute a custom metric. │ │ ├── .report_metrics(...) - Report a set of metrics for our estimator. │ │ └── .plot │ │ ├── .precision_recall(...) - Plot the precision-recall curve. │ │ └── .roc(...) - Plot the ROC curve. │ │ │ │ │ │ Legend: │ │ (↗︎) higher is better (↘︎) lower is better │ ╰─────────────────────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 99-101 .. code-block:: Python report.metrics.plot.help() .. rst-class:: sphx-glr-script-out .. code-block:: none ╭───────────────────── Available plot methods ──────────────────────╮ │ report.metrics.plot │ │ ├── .precision_recall(...) - Plot the precision-recall curve. │ │ └── .roc(...) - Plot the ROC curve. │ ╰───────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 102-110 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.report_metrics` method. .. GENERATED FROM PYTHON SOURCE LINES 111-118 .. code-block:: Python import time start = time.time() metric_report = report.metrics.report_metrics(pos_label=pos_label) end = time.time() metric_report .. raw:: html
Metric Precision (↗︎) Recall (↗︎) ROC AUC (↗︎) Brier score (↘︎)
HistGradientBoostingClassifier 0.694588 0.463058 0.943464 0.034585


.. GENERATED FROM PYTHON SOURCE LINES 119-121 .. 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: 3.50 seconds .. GENERATED FROM PYTHON SOURCE LINES 122-129 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 130-136 .. code-block:: Python start = time.time() metric_report = report.metrics.report_metrics(pos_label=pos_label) end = time.time() metric_report .. raw:: html
Metric Precision (↗︎) Recall (↗︎) ROC AUC (↗︎) Brier score (↘︎)
HistGradientBoostingClassifier 0.694588 0.463058 0.943464 0.034585


.. GENERATED FROM PYTHON SOURCE LINES 137-139 .. 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 140-142 Since we obtain a pandas dataframe, we can also use the plotting interface of pandas. .. GENERATED FROM PYTHON SOURCE LINES 143-149 .. code-block:: Python import matplotlib.pyplot as plt ax = metric_report.T.plot.barh() ax.set_title("Metrics report") plt.tight_layout() .. 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 .. 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 .. raw:: html
Metric Log loss (↘︎)
HistGradientBoostingClassifier 0.12273


.. 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: 0.04 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 .. raw:: html
Metric Log loss (↘︎)
HistGradientBoostingClassifier 0.12273


.. 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: 1.12 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") .. raw:: html
Metric Log loss (↘︎)
HistGradientBoostingClassifier 0.09905


.. GENERATED FROM PYTHON SOURCE LINES 184-187 In the case where we are interested in computing the metrics on a completely new set of data, we can use the `data_source="X_y"` parameter. In addition, we need to provide a `X` and `y` parameters. .. GENERATED FROM PYTHON SOURCE LINES 188-196 .. code-block:: Python start = time.time() metric_report = report.metrics.report_metrics( data_source="X_y", X=X_test, y=y_test, pos_label=pos_label ) end = time.time() metric_report .. raw:: html
Metric Precision (↗︎) Recall (↗︎) ROC AUC (↗︎) Brier score (↘︎)
HistGradientBoostingClassifier 0.694588 0.463058 0.943464 0.034585


.. GENERATED FROM PYTHON SOURCE LINES 197-199 .. 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: 3.62 seconds .. GENERATED FROM PYTHON SOURCE LINES 200-203 As in the other case, we rely on the cache to avoid recomputing the predictions. Internally, we compute a hash of the input data to be sure that we can hit the cache in a consistent way. .. GENERATED FROM PYTHON SOURCE LINES 206-213 .. code-block:: Python start = time.time() metric_report = report.metrics.report_metrics( data_source="X_y", X=X_test, y=y_test, pos_label=pos_label ) end = time.time() metric_report .. raw:: html
Metric Precision (↗︎) Recall (↗︎) ROC AUC (↗︎) Brier score (↘︎)
HistGradientBoostingClassifier 0.694588 0.463058 0.943464 0.034585


.. GENERATED FROM PYTHON SOURCE LINES 214-216 .. 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: 0.17 seconds .. GENERATED FROM PYTHON SOURCE LINES 217-226 .. note:: In this last example, we rely on computing the hash of the input data. Therefore, there is a trade-off: the computation of the hash is not free and it might be faster to compute the predictions instead. 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 227-241 .. 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 242-246 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 247-252 .. code-block:: Python import numpy as np rng = np.random.default_rng(42) amount = rng.integers(low=100, high=1000, size=len(y_test)) .. GENERATED FROM PYTHON SOURCE LINES 253-255 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 256-258 .. code-block:: Python report.metrics.accuracy() .. raw:: html
Metric Accuracy (↗︎)
HistGradientBoostingClassifier 0.953127


.. GENERATED FROM PYTHON SOURCE LINES 259-260 We can now compute the cost of our operational decision. .. GENERATED FROM PYTHON SOURCE LINES 261-271 .. code-block:: Python start = time.time() cost = report.metrics.custom_metric( metric_function=operational_decision_cost, metric_name="Operational Decision Cost", response_method="predict", amount=amount, ) end = time.time() cost .. raw:: html
Metric Operational Decision Cost
HistGradientBoostingClassifier -131336.66


.. GENERATED FROM PYTHON SOURCE LINES 272-274 .. 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 275-276 Let's now clean the cache and see if it is faster. .. GENERATED FROM PYTHON SOURCE LINES 277-279 .. code-block:: Python report.clear_cache() .. GENERATED FROM PYTHON SOURCE LINES 280-290 .. code-block:: Python start = time.time() cost = report.metrics.custom_metric( metric_function=operational_decision_cost, metric_name="Operational Decision Cost", response_method="predict", amount=amount, ) end = time.time() cost .. raw:: html
Metric Operational Decision Cost
HistGradientBoostingClassifier -131336.66


.. GENERATED FROM PYTHON SOURCE LINES 291-293 .. 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: 1.09 seconds .. GENERATED FROM PYTHON SOURCE LINES 294-297 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 298-308 .. code-block:: Python report.metrics.report_metrics( scoring=["precision", "recall", operational_decision_cost], pos_label=pos_label, scoring_kwargs={ "amount": amount, "response_method": "predict", "metric_name": "Operational Decision Cost", }, ) .. raw:: html
Metric Operational Decision Cost Operational Decision Cost Operational Decision Cost
HistGradientBoostingClassifier 0.694588 0.463058 -131336.66


.. GENERATED FROM PYTHON SOURCE LINES 309-313 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 314-327 .. code-block:: Python from sklearn.metrics import make_scorer, f1_score f1_scorer = make_scorer( f1_score, response_method="predict", metric_name="F1 Score", pos_label=pos_label ) operational_decision_cost_scorer = make_scorer( operational_decision_cost, response_method="predict", metric_name="Operational Decision Cost", amount=amount, ) report.metrics.report_metrics(scoring=[f1_scorer, operational_decision_cost_scorer]) .. raw:: html
Metric F1 Score Operational Decision Cost
HistGradientBoostingClassifier 0.55567 -131336.66


.. GENERATED FROM PYTHON SOURCE LINES 328-334 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 335-337 .. code-block:: Python report.metrics.plot.help() .. rst-class:: sphx-glr-script-out .. code-block:: none ╭───────────────────── Available plot methods ──────────────────────╮ │ report.metrics.plot │ │ ├── .precision_recall(...) - Plot the precision-recall curve. │ │ └── .roc(...) - Plot the ROC curve. │ ╰───────────────────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 338-339 Let's start by plotting the ROC curve for our binary classification task. .. GENERATED FROM PYTHON SOURCE LINES 340-343 .. code-block:: Python display = report.metrics.plot.roc(pos_label=pos_label) plt.tight_layout() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_002.png :alt: plot estimator report :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 344-348 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 349-351 .. code-block:: Python display .. rst-class:: sphx-glr-script-out .. code-block:: none skore.RocCurveDisplay(...) .. GENERATED FROM PYTHON SOURCE LINES 352-354 .. code-block:: Python display.help() .. rst-class:: sphx-glr-script-out .. code-block:: none ╭─ RocCurveDisplay for HistGradientBoostingClassifier ─╮ │ display │ │ ├── Attributes │ │ │ ├── .figure_ │ │ │ ├── .ax_ │ │ │ ├── .chance_level_ │ │ │ └── .lines_ │ │ └── Methods │ │ └── .plot(...) - Plot visualization. │ ╰──────────────────────────────────────────────────────╯ .. GENERATED FROM PYTHON SOURCE LINES 355-360 .. code-block:: Python display.plot() display.ax_.set_title("Example of a ROC curve") display.figure_ plt.tight_layout() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_003.png :alt: 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 361-365 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 366-372 .. code-block:: Python start = time.time() # we already trigger the computation of the predictions in a previous call report.metrics.plot.roc(pos_label=pos_label) plt.tight_layout() end = time.time() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_004.png :alt: plot estimator report :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 373-375 .. 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: 0.03 seconds .. GENERATED FROM PYTHON SOURCE LINES 376-377 Now, let's clean the cache and check if we get a slowdown. .. GENERATED FROM PYTHON SOURCE LINES 378-380 .. code-block:: Python report.clear_cache() .. GENERATED FROM PYTHON SOURCE LINES 381-386 .. code-block:: Python start = time.time() report.metrics.plot.roc(pos_label=pos_label) plt.tight_layout() end = time.time() .. image-sg:: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_005.png :alt: plot estimator report :srcset: /auto_examples/model_evaluation/images/sphx_glr_plot_estimator_report_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 387-389 .. 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: 1.13 seconds .. GENERATED FROM PYTHON SOURCE LINES 390-391 As expected, since we need to recompute the predictions, it takes more time. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 20.208 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 `_