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Visualizations with Display Objects

In this example, we will construct display objects, ConfusionMatrixDisplay, RocCurveDisplay, and PrecisionRecallDisplay directly from their respective metrics. This is an alternative to using their corresponding plot functions when a model’s predictions are already computed or expensive to compute. Note that this is advanced usage, and in general we recommend using their respective plot functions.

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

Load Data and train model

For this example, we load a blood transfusion service center data set from OpenML. This is a binary classification problem where the target is whether an individual donated blood. Then the data is split into a train and test dataset and a logistic regression is fitted with the train dataset.

from sklearn.datasets import fetch_openml
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

X, y = fetch_openml(data_id=1464, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y)

clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0))
clf.fit(X_train, y_train)

Pipeline(steps=[('standardscaler', StandardScaler()),
                ('logisticregression', LogisticRegression(random_state=0))])
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Parameters
steps steps: list of tuples

List of (name of step, estimator) tuples that are to be chained in
sequential order. To be compatible with the scikit-learn API, all steps
must define `fit`. All non-last steps must also define `transform`. See
:ref:`Combining Estimators <combining_estimators>` for more details. 		[('standardscaler', ...), ('logisticregression', ...)]
transform_input transform_input: list of str, default=None

The names of the :term:`metadata` parameters that should be transformed by the
pipeline before passing it to the step consuming it.

This enables transforming some input arguments to ``fit`` (other than ``X``)
to be transformed by the steps of the pipeline up to the step which requires
them. Requirement is defined via :ref:`metadata routing <metadata_routing>`.
For instance, this can be used to pass a validation set through the pipeline.

You can only set this if metadata routing is enabled, which you
can enable using ``sklearn.set_config(enable_metadata_routing=True)``.

.. versionadded:: 1.6 		None
memory memory: str or object with the joblib.Memory interface, default=None

Used to cache the fitted transformers of the pipeline. The last step
will never be cached, even if it is a transformer. By default, no
caching is performed. If a string is given, it is the path to the
caching directory. Enabling caching triggers a clone of the transformers
before fitting. Therefore, the transformer instance given to the
pipeline cannot be inspected directly. Use the attribute ``named_steps``
or ``steps`` to inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming. See
:ref:`sphx_glr_auto_examples_neighbors_plot_caching_nearest_neighbors.py`
for an example on how to enable caching. 		None
verbose verbose: bool, default=False

If True, the time elapsed while fitting each step will be printed as it
is completed. 		False
Fitted attributes
Name Type Value
classes_ classes_: ndarray of shape (n_classes,)

The classes labels. Only exist if the last step of the pipeline is a
classifier. 		ndarray[object](2,) ['1','2']
feature_names_in_ feature_names_in_: ndarray of shape (`n_features_in_`,)

Names of features seen during :term:`fit`. Only defined if the
underlying estimator exposes such an attribute when fit.

.. versionadded:: 1.0 		ndarray[object](4,) ['V1','V2','V3','V4']
n_features_in_ n_features_in_: int

Number of features seen during :term:`fit`. Only defined if the
underlying first estimator in `steps` exposes such an attribute
when fit.

.. versionadded:: 0.24 		int 4
Parameters
copy copy: bool, default=True

If False, try to avoid a copy and do inplace scaling instead.
This is not guaranteed to always work inplace; e.g. if the data is
not a NumPy array or scipy.sparse CSR matrix, a copy may still be
returned. 		True
with_mean with_mean: bool, default=True

If True, center the data before scaling.
This does not work (and will raise an exception) when attempted on
sparse matrices, because centering them entails building a dense
matrix which in common use cases is likely to be too large to fit in
memory. 		True
with_std with_std: bool, default=True

If True, scale the data to unit variance (or equivalently,
unit standard deviation). 		True
Fitted attributes
Name Type Value
feature_names_in_ feature_names_in_: ndarray of shape (`n_features_in_`,)

Names of features seen during :term:`fit`. Defined only when `X`
has feature names that are all strings.

.. versionadded:: 1.0 		ndarray[object](4,) ['V1','V2','V3','V4']
mean_ mean_: ndarray of shape (n_features,) or None

The mean value for each feature in the training set.
Equal to ``None`` when ``with_mean=False`` and ``with_std=False``. 		ndarray[float64](4,) [ 9.82, 5.32,1330.66, 34.18]
n_features_in_ n_features_in_: int

Number of features seen during :term:`fit`.

.. versionadded:: 0.24 		int 4
n_samples_seen_ n_samples_seen_: int or ndarray of shape (n_features,)

The number of samples processed by the estimator for each feature.
If there are no missing samples, the ``n_samples_seen`` will be an
integer, otherwise it will be an array of dtype int. If
`sample_weights` are used it will be a float (if no missing data)
or an array of dtype float that sums the weights seen so far.
Will be reset on new calls to fit, but increments across
``partial_fit`` calls. 		float64 561
scale_ scale_: ndarray of shape (n_features,) or None

Per feature relative scaling of the data to achieve zero mean and unit
variance. Generally this is calculated using `np.sqrt(var_)`. If a
variance is zero, we can't achieve unit variance, and the data is left
as-is, giving a scaling factor of 1. `scale_` is equal to `None`
when `with_std=False`.

.. versionadded:: 0.17
*scale_* 		ndarray[float64](4,) [ 8.3 , 5.34,1335.97, 23.33]
var_ var_: ndarray of shape (n_features,) or None

The variance for each feature in the training set. Used to compute
`scale_`. Equal to ``None`` when ``with_mean=False`` and
``with_std=False``. 		ndarray[float64](4,) [ 68.95, 28.56,1784826.48, 544.31]
4 features
V1
V2
V3
V4
Parameters
random_state random_state: int, RandomState instance, default=None

Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the
data. See :term:`Glossary <random_state>` for details. 		0
penalty penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'

Specify the norm of the penalty:

- `None`: no penalty is added;
- `'l2'`: add an L2 penalty term and it is the default choice;
- `'l1'`: add an L1 penalty term;
- `'elasticnet'`: both L1 and L2 penalty terms are added.

.. warning::
Some penalties may not work with some solvers. See the parameter
`solver` below, to know the compatibility between the penalty and
solver.

.. versionadded:: 0.19
l1 penalty with SAGA solver (allowing 'multinomial' + L1)

.. deprecated:: 1.8
`penalty` was deprecated in version 1.8 and will be removed in 1.10.
Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty='l2'`,
`l1_ratio=1` for `penalty='l1'`, `l1_ratio` set to any float between 0 and 1
for `penalty='elasticnet'`, and `C=np.inf` for `penalty=None`. 		'deprecated'
C C: float, default=1.0

Inverse of regularization strength; must be a positive float.
Like in support vector machines, smaller values specify stronger
regularization. `C=np.inf` results in unpenalized logistic regression.
For a visual example on the effect of tuning the `C` parameter
with an L1 penalty, see:
:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`. 		1.0
l1_ratio l1_ratio: float, default=0.0

The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting
`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.
Any value between 0 and 1 gives an Elastic-Net penalty of the form
`l1_ratio * L1 + (1 - l1_ratio) * L2`.

.. warning::
Certain values of `l1_ratio`, i.e. some penalties, may not work with some
solvers. See the parameter `solver` below, to know the compatibility between
the penalty and solver.

.. versionchanged:: 1.8
Default value changed from None to 0.0.

.. deprecated:: 1.8
`None` is deprecated and will be removed in version 1.10. Always use
`l1_ratio` to specify the penalty type. 		0.0
dual dual: bool, default=False

Dual (constrained) or primal (regularized, see also
:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation
is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`
when n_samples > n_features. 		False
tol tol: float, default=1e-4

Tolerance for stopping criteria. 		0.0001
fit_intercept fit_intercept: bool, default=True

Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function. 		True
intercept_scaling intercept_scaling: float, default=1

Useful only when the solver `liblinear` is used
and `self.fit_intercept` is set to `True`. In this case, `x` becomes
`[x, self.intercept_scaling]`,
i.e. a "synthetic" feature with constant value equal to
`intercept_scaling` is appended to the instance vector.
The intercept becomes
``intercept_scaling * synthetic_feature_weight``.

.. note::
The synthetic feature weight is subject to L1 or L2
regularization as all other features.
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) `intercept_scaling` has to be increased. 		1
class_weight class_weight: dict or 'balanced', default=None

Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.

The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``.

Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.

.. versionadded:: 0.17
*class_weight='balanced'* 		None
solver solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, default='lbfgs'

Algorithm to use in the optimization problem. Default is 'lbfgs'.
To choose a solver, you might want to consider the following aspects:

- 'lbfgs' is a good default solver because it works reasonably well for a wide
class of problems.
- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except
'liblinear' minimize the full multinomial loss, 'liblinear' will raise an
error.
- 'newton-cholesky' is a good choice for
`n_samples` >> `n_features * n_classes`, especially with one-hot encoded
categorical features with rare categories. Be aware that the memory usage
of this solver has a quadratic dependency on `n_features * n_classes`
because it explicitly computes the full Hessian matrix.
- For small datasets, 'liblinear' is a good choice, whereas 'sag'
and 'saga' are faster for large ones;
- 'liblinear' can only handle binary classification by default. To apply a
one-versus-rest scheme for the multiclass setting one can wrap it with the
:class:`~sklearn.multiclass.OneVsRestClassifier`.

.. warning::
The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`
for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for
Elastic-Net) and on (multinomial) multiclass support:

================= ======================== ======================
solver l1_ratio multinomial multiclass
================= ======================== ======================
'lbfgs' l1_ratio=0 yes
'liblinear' l1_ratio=1 or l1_ratio=0 no
'newton-cg' l1_ratio=0 yes
'newton-cholesky' l1_ratio=0 yes
'sag' l1_ratio=0 yes
'saga' 0<=l1_ratio<=1 yes
================= ======================== ======================

.. note::
'sag' and 'saga' fast convergence is only guaranteed on features
with approximately the same scale. You can preprocess the data with
a scaler from :mod:`sklearn.preprocessing`.

.. seealso::
Refer to the :ref:`User Guide <Logistic_regression>` for more
information regarding :class:`LogisticRegression` and more specifically the
:ref:`Table <logistic_regression_solvers>`
summarizing solver/penalty supports.

.. versionadded:: 0.17
Stochastic Average Gradient (SAG) descent solver. Multinomial support in
version 0.18.
.. versionadded:: 0.19
SAGA solver.
.. versionchanged:: 0.22
The default solver changed from 'liblinear' to 'lbfgs' in 0.22.
.. versionadded:: 1.2
newton-cholesky solver. Multinomial support in version 1.6. 		'lbfgs'
max_iter max_iter: int, default=100

Maximum number of iterations taken for the solvers to converge. 		100
verbose verbose: int, default=0

For the liblinear and lbfgs solvers set verbose to any positive
number for verbosity. 		0
warm_start warm_start: bool, default=False

When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
Useless for liblinear solver. See :term:`the Glossary <warm_start>`.

.. versionadded:: 0.17
*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers. 		False
n_jobs n_jobs: int, default=None

Does not have any effect.

.. deprecated:: 1.8
`n_jobs` is deprecated in version 1.8 and will be removed in 1.10. 		None
Fitted attributes
Name Type Value
classes_ classes_: ndarray of shape (n_classes, )

A list of class labels known to the classifier. 		ndarray[object](2,) ['1','2']
coef_ coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)

Coefficients of the features in the decision function.

`coef_` is of shape (1, n_features) when the given problem is binary.

By default, it will be created as a dense array, but can be turned to
sparse (CSR format) through :meth:`sparsify` (which can be beneficial
under L1 regularization when many coefficients are zero), and back to
dense through :meth:`densify`. 		ndarray[float64](1, 4) [[-0.74, 0.38, 0.38,-0.53]]
intercept_ intercept_: ndarray of shape (1,) or (n_classes,)

Intercept (a.k.a. bias) added to the decision function.

If `fit_intercept` is set to False, the intercept is set to zero.
`intercept_` is of shape (1,) when the given problem is binary. 		ndarray[float64](1,) [-1.41]
n_features_in_ n_features_in_: int

Number of features seen during :term:`fit`.

.. versionadded:: 0.24 		int 4
n_iter_ n_iter_: ndarray of shape (1, )

Actual number of iterations for all classes.

.. versionchanged:: 0.20

In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
``max_iter``. ``n_iter_`` will now report at most ``max_iter``. 		ndarray[int32](1,) [8]


Create ConfusionMatrixDisplay

With the fitted model, we compute the predictions of the model on the test dataset. These predictions are used to compute the confusion matrix which is plotted with the ConfusionMatrixDisplay

from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix

y_pred = clf.predict(X_test)
cm = confusion_matrix(y_test, y_pred)

cm_display = ConfusionMatrixDisplay(cm).plot()
plot display object visualization

Create RocCurveDisplay

The roc curve requires either the probabilities or the non-thresholded decision values from the estimator. Since the logistic regression provides a decision function, we will use it to plot the roc curve:

from sklearn.metrics import RocCurveDisplay, roc_curve

y_score = clf.decision_function(X_test)

fpr, tpr, _ = roc_curve(y_test, y_score, pos_label=clf.classes_[1])
roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()
plot display object visualization

Create PrecisionRecallDisplay

Similarly, the precision recall curve can be plotted using y_score from the prevision sections.

from sklearn.metrics import PrecisionRecallDisplay, precision_recall_curve

prec, recall, _ = precision_recall_curve(y_test, y_score, pos_label=clf.classes_[1])
pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot()
plot display object visualization

Combining the display objects into a single plot

The display objects store the computed values that were passed as arguments. This allows for the visualizations to be easily combined using matplotlib’s API. In the following example, we place the displays next to each other in a row.

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 8))

roc_display.plot(ax=ax1)
pr_display.plot(ax=ax2)
plt.show()
plot display object visualization

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Related examples

Precision-Recall

ROC Curve with Visualization API

Multiclass Receiver Operating Characteristic (ROC)

Post-tuning the decision threshold for cost-sensitive learning

© 2007–2026 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/1.9/auto_examples/miscellaneous/plot_display_object_visualization.html

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