Automatic detection of modelling issues

skore can automatically detect common modeling pitfalls such as overfitting and underfitting. This example walks through the checks accessor: how to run checks, how to read the detected issues, and how to mute specific checks.

We use a purely non-linear regression target and deliberately pick models that fail in known ways:

• a linear model that cannot capture the non-linearity → underfitting,

• a single deep decision tree that memorizes the training set perfectly and fails to generalize → overfitting.

Setup

The target is a product of trigonometric functions of the first two features: completely invisible to a linear model, yet easy to memorize for an unconstrained tree.

import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor

rng = np.random.default_rng(42)
n_samples = 500
X = rng.uniform(0, 1, (n_samples, 5))
y = np.sin(2 * np.pi * X[:, 0]) * np.cos(2 * np.pi * X[:, 1]) + rng.normal(
    0, 0.1, n_samples
)

linear = LinearRegression()
deep_tree = DecisionTreeRegressor(random_state=42)

Calling summarize() explicitly

Every report exposes a checks accessor which provides access to several methods:

• summarize() to run checks and get a summary of the findings

• add() to add custom checks

• remove() to remove checks

• available() to list the available checks

Let’s use summarize() to see what issues can be found for the linear model.

from skore import evaluate

linear_report = evaluate(linear, X, y)
linear_report

LinearRegression()

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1 issue(s), 1 tip(s), 2 passed, 7 not applicable, 0 ignored.

linear_report.checks.summarize()

linear_report.metrics.summarize(data_source="both").frame()

	LinearRegression (train)	LinearRegression (test)
Metric
R²	0.001906	-0.015818
RMSE	0.522214	0.504156
MAE	0.423723	0.406739
MAPE	1.426344	1.032250
Fit time (s)	0.001114	0.001114
Predict time (s)	0.000263	0.000391

The linear model is flagged for underfitting: its scores are on par between train and test, and not significantly better than a dummy baseline.

Let’s now inspect the deep tree model.

tree_report = evaluate(deep_tree, X, y)
tree_report.checks.summarize()

tree_report.metrics.summarize(data_source="both").frame()

	DecisionTreeRegressor (train)	DecisionTreeRegressor (test)
Metric
R²	1.000000	0.783887
RMSE	0.000000	0.232540
MAE	0.000000	0.180261
MAPE	0.000000	1.052768
Fit time (s)	0.003189	0.003189
Predict time (s)	0.000308	0.000245

The deep tree is flagged for overfitting: it achieves a perfect score on train but degrades on test. For this model, the tip about coefficients is not applicable and appears under the Not Applicable section of the checks summary.

Ignoring specific checks

Each check has a stable code (e.g. SKD001, SKD002). You can mute individual checks per call:

tree_report.checks.summarize(ignore=["SKD001"])

Or globally, so that every subsequent summarize() call skips them:

import skore

with skore.configuration(ignore_checks=["SKD001"]):
    checks_summary = tree_report.checks.summarize()
checks_summary

Checks on a CrossValidationReport

When splitter is an integer, evaluate() returns a CrossValidationReport. Checks aggregate issues across folds.

cv_report = evaluate(deep_tree, X, y, splitter=5)
cv_report.checks.summarize()

Checks on a ComparisonReport

Passing a list of estimators returns a ComparisonReport. Issues are grouped by sub-report.

comparison_report = evaluate([linear, deep_tree], X, y)
comparison_report.checks.summarize()