EstimatorReport: Get insights from any scikit-learn estimator

This example shows how the skore.EstimatorReport class can be used to quickly get insights from any scikit-learn estimator.

Loading our dataset and defining our estimator

First, we load a dataset from skrub. Our goal is to predict if a healthcare manufacturing company paid medical doctors or hospitals, in order to detect potential conflicts of interest.

from skrub.datasets import fetch_open_payments

dataset = fetch_open_payments()
df = dataset.X
y = dataset.y

Downloading 'open_payments' from https://github.com/skrub-data/skrub-data-files/raw/refs/heads/main/open_payments.zip (attempt 1/3)

from skrub import TableReport

TableReport(df)

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TableReport(y.to_frame())

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Looking at the distributions of the target, we observe that this classification task is quite imbalanced. This 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”.

pos_label, neg_label = "allowed", "disallowed"

Let’s create a predictive model. Thankfully, skrub provides a convenient function (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.

from skrub import tabular_pipeline

estimator = tabular_pipeline("classifier")
estimator

Pipeline(steps=[('tablevectorizer',
                 TableVectorizer(low_cardinality=ToCategorical())),
                ('histgradientboostingclassifier',
                 HistGradientBoostingClassifier())])

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Introducing the EstimatorReport

Let’s gather some insights from our predictive model. We can use skore.evaluate() for this: the function will perform a train-test split and create a EstimatorReport containing the model fitted on the training data, ready to investigate.

from skore import evaluate

# Reserve 20% of the data for the test set
report = evaluate(estimator, X=df, y=y, pos_label=pos_label, splitter=0.2)
report

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.

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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").

Once the report is created, we get some information regarding the available tools allowing us to get some insights on our model by calling the help() method.

report.help()

Be aware that we can access the help for each individual sub-accessor. For instance:

report.metrics.help()

Measuring model performance

Let’s have a first look at the statistical performance of our model. skore knows that we are doing classification, and can give us an array of classic ML metrics, all at once, with summarize():

import time

start = time.time()
metric_report = report.metrics.summarize().frame()
end = time.time()
metric_report

	HistGradientBoostingClassifier
Metric
Accuracy	0.950721
Precision	0.721154
Recall	0.449550
ROC AUC	0.940003
Log loss	0.130316
Brier score	0.036849
Fit time (s)	10.457895
Predict time (s)	1.270659

print(f"Time taken to compute the metrics: {end - start:.2f} seconds")

Time taken to compute the metrics: 0.00 seconds

Since the output is a pandas dataframe, we can also use the plotting interface of pandas.

ax = metric_report.plot.barh()
_ = ax.set_title("Metrics report")

An interesting feature of the skore.EstimatorReport is its caching mechanism. Indeed, when we have a large enough dataset, computing the predictions for a model can be expensive. To amortize this cost, the report will cache the predictions when it is first created; this way, calculations that need the model predictions can get them from the cache and save a lot of time. This is why the metrics computation above is so fast.

When the model is fitted or the predictions are computed, we additionally store the time the operation took:

report.metrics.timings()

{'fit_time': 10.457895054000005, 'predict_time_train': 4.923631041999954, 'predict_time_test': 1.2706592290000458}

By default, the metrics are computed on the test set only, but we can also compute them on the train set:

report.metrics.log_loss(data_source="train")

0.09608487593388408

Defining custom metrics

skore can compute user-defined metrics as well. It accepts metrics in the form of scikit-learn scorers, i.e. functions taking estimator, X and y (and optional keyword arguments). Let’s take a look at an example.

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

In our example use case, each classification decision we make has a cost. The function above models this by translating the confusion matrix into a cost matrix; this cost also depends on an extra parameter named amount, to illustrate that skore can handle custom metrics with non-standard arguments. Let’s test adding this metric to our report.

import numpy as np
from sklearn.metrics import make_scorer

rng = np.random.default_rng(42)
amount = rng.integers(low=100, high=1000, size=len(report.y_test))

# We use `make_scorer` to convert the metric to the right format (a function
# that takes `estimator`, `X`, `y`)
report.metrics.add(metric=make_scorer(operational_decision_cost, amount=amount))

Our custom metric is now registered in the report, and will be shown in the summary. In fact, since the underlying metric function takes y_pred as input, skore can use the cached predictions again to speed up the computation.

# The metric name is derived from the function name unless it is explicitly given
report.metrics.summarize().frame()

	HistGradientBoostingClassifier
Metric
Operational Decision Cost	-135954.600000
Accuracy	0.950721
Precision	0.721154
Recall	0.449550
ROC AUC	0.940003
Log loss	0.130316
Brier score	0.036849
Fit time (s)	10.457895
Predict time (s)	1.270659

Effortless one-liner plotting

The skore.EstimatorReport class also implements a number of the most common data science plots. As for the metrics, we only provide the meaningful set of plots for the provided estimator.

report.metrics.help()

Let’s plot the ROC curve for our binary classification task.

display = report.metrics.roc()
display.plot()

<Figure size 600x750 with 1 Axes>

The plot functionality is built upon the scikit-learn Display objects. We return those Display objects (slightly modified to improve the UI) in case we want to tweak some of the plot properties. We can have a quick look at the available attributes and methods by calling the help method.

display.help()

fig = display.plot()
fig.axes[0].set_title("Example of a ROC curve")
fig

<Figure size 600x750 with 1 Axes>

Similarly to the metrics, the cache allows us to avoid recomputing the model predictions, which speeds up the display generation.

start = time.time()
display = report.metrics.roc()
_ = display.plot()
end = time.time()
print(f"Time taken to compute the ROC curve: {end - start:.2f} seconds")

Time taken to compute the ROC curve: 0.10 seconds

You can learn more about the cache system in the corresponding example: Cache mechanism.

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.

Let’s start with a basic confusion matrix:

cm_display = report.metrics.confusion_matrix()
cm_display.plot()

<Figure size 600x600 with 1 Axes>

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.

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:

cm_display.plot(threshold_value=0.3)

<Figure size 600x600 with 1 Axes>

We can normalize the confusion matrix to get percentages instead of raw counts. Here we normalize by true labels (rows):

cm_display.plot(normalize="true")

<Figure size 600x600 with 1 Axes>

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:

cm_display.set_style(heatmap_kwargs={"cmap": "Greens", "fmt": ".2e"})
cm_display.plot()

<Figure size 600x600 with 1 Axes>

Finally, the confusion matrix can also be exported as a pandas DataFrame for further analysis:

cm_display.frame()

	true_label	predicted_label	value
0	allowed	allowed	450
1	allowed	disallowed	551
2	disallowed	allowed	174
3	disallowed	disallowed	13537

See also

For using the EstimatorReport to inspect your models, see EstimatorReport: Inspecting your models with the feature importance.