sklearn.metrics.PredictionErrorDisplay

class sklearn.metrics.PredictionErrorDisplay(*, y_true, y_pred)

Visualization of the prediction error of a regression model.

This tool can display “residuals vs predicted” or “actual vs predicted” using scatter plots to qualitatively assess the behavior of a regressor, preferably on held-out data points.

See the details in the docstrings of from_estimator or from_predictions to create a visualizer. All parameters are stored as attributes.

For general information regarding scikit-learn visualization tools, read more in the Visualization Guide. For details regarding interpreting these plots, refer to the Model Evaluation Guide.

New in version 1.2.

Parameters:

y_truendarray of shape (n_samples,)

True values.

y_predndarray of shape (n_samples,)

Prediction values.

Attributes:

line_matplotlib Artist

Optimal line representing y_true == y_pred. Therefore, it is a diagonal line for kind="predictions" and a horizontal line for kind="residuals".

errors_lines_matplotlib Artist or None

Residual lines. If with_errors=False, then it is set to None.

scatter_matplotlib Artist

Scatter data points.

ax_matplotlib Axes

Axes with the different matplotlib axis.

figure_matplotlib Figure

Figure containing the scatter and lines.

See also

PredictionErrorDisplay.from_estimator

Prediction error visualization given an estimator and some data.

PredictionErrorDisplay.from_predictions

Prediction error visualization given the true and predicted targets.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import PredictionErrorDisplay
>>> X, y = load_diabetes(return_X_y=True)
>>> ridge = Ridge().fit(X, y)
>>> y_pred = ridge.predict(X)
>>> display = PredictionErrorDisplay(y_true=y, y_pred=y_pred)
>>> display.plot()
<...>
>>> plt.show()

Methods

from_estimator(estimator, X, y, *[, kind, ...])	Plot the prediction error given a regressor and some data.
from_predictions(y_true, y_pred, *[, kind, ...])	Plot the prediction error given the true and predicted targets.
plot([ax, kind, scatter_kwargs, line_kwargs])	Plot visualization.

classmethod from_estimator(estimator, X, y, *, kind='residual_vs_predicted', subsample=1000, random_state=None, ax=None, scatter_kwargs=None, line_kwargs=None)

Plot the prediction error given a regressor and some data.

For general information regarding scikit-learn visualization tools, read more in the Visualization Guide. For details regarding interpreting these plots, refer to the Model Evaluation Guide.

New in version 1.2.

Parameters:

estimatorestimator instance

Fitted regressor or a fitted Pipeline in which the last estimator is a regressor.

X{array-like, sparse matrix} of shape (n_samples, n_features)

Input values.

yarray-like of shape (n_samples,)

Target values.

kind{“actual_vs_predicted”, “residual_vs_predicted”}, default=”residual_vs_predicted”

The type of plot to draw:

• “actual_vs_predicted” draws the the observed values (y-axis) vs. the predicted values (x-axis).

• “residual_vs_predicted” draws the residuals, i.e difference between observed and predicted values, (y-axis) vs. the predicted values (x-axis).

subsamplefloat, int or None, default=1_000

Sampling the samples to be shown on the scatter plot. If float, it should be between 0 and 1 and represents the proportion of the original dataset. If int, it represents the number of samples display on the scatter plot. If None, no subsampling will be applied. by default, a 1000 samples or less will be displayed.

random_stateint or RandomState, default=None

Controls the randomness when subsample is not None. See Glossary for details.

axmatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

scatter_kwargsdict, default=None

Dictionary with keywords passed to the matplotlib.pyplot.scatter call.

line_kwargsdict, default=None

Dictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.

Returns:

displayPredictionErrorDisplay

Object that stores the computed values.

See also

PredictionErrorDisplay

Prediction error visualization for regression.

PredictionErrorDisplay.from_predictions

Prediction error visualization given the true and predicted targets.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import PredictionErrorDisplay
>>> X, y = load_diabetes(return_X_y=True)
>>> ridge = Ridge().fit(X, y)
>>> disp = PredictionErrorDisplay.from_estimator(ridge, X, y)
>>> plt.show()

classmethod from_predictions(y_true, y_pred, *, kind='residual_vs_predicted', subsample=1000, random_state=None, ax=None, scatter_kwargs=None, line_kwargs=None)

Plot the prediction error given the true and predicted targets.

For general information regarding scikit-learn visualization tools, read more in the Visualization Guide. For details regarding interpreting these plots, refer to the Model Evaluation Guide.

New in version 1.2.

Parameters:

y_truearray-like of shape (n_samples,)

True target values.

y_predarray-like of shape (n_samples,)

Predicted target values.

kind{“actual_vs_predicted”, “residual_vs_predicted”}, default=”residual_vs_predicted”

The type of plot to draw:

• “actual_vs_predicted” draws the the observed values (y-axis) vs. the predicted values (x-axis).

• “residual_vs_predicted” draws the residuals, i.e difference between observed and predicted values, (y-axis) vs. the predicted values (x-axis).

subsamplefloat, int or None, default=1_000

Sampling the samples to be shown on the scatter plot. If float, it should be between 0 and 1 and represents the proportion of the original dataset. If int, it represents the number of samples display on the scatter plot. If None, no subsampling will be applied. by default, a 1000 samples or less will be displayed.

random_stateint or RandomState, default=None

Controls the randomness when subsample is not None. See Glossary for details.

axmatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

scatter_kwargsdict, default=None

Dictionary with keywords passed to the matplotlib.pyplot.scatter call.

line_kwargsdict, default=None

Dictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.

Returns:

displayPredictionErrorDisplay

Object that stores the computed values.

See also

PredictionErrorDisplay

Prediction error visualization for regression.

PredictionErrorDisplay.from_estimator

Prediction error visualization given an estimator and some data.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.metrics import PredictionErrorDisplay
>>> X, y = load_diabetes(return_X_y=True)
>>> ridge = Ridge().fit(X, y)
>>> y_pred = ridge.predict(X)
>>> disp = PredictionErrorDisplay.from_predictions(y_true=y, y_pred=y_pred)
>>> plt.show()

plot(ax=None, *, kind='residual_vs_predicted', scatter_kwargs=None, line_kwargs=None)

Plot visualization.

Extra keyword arguments will be passed to matplotlib’s plot.

Parameters:

axmatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

kind{“actual_vs_predicted”, “residual_vs_predicted”}, default=”residual_vs_predicted”

The type of plot to draw:

• “actual_vs_predicted” draws the the observed values (y-axis) vs. the predicted values (x-axis).

• “residual_vs_predicted” draws the residuals, i.e difference between observed and predicted values, (y-axis) vs. the predicted values (x-axis).

scatter_kwargsdict, default=None

Dictionary with keywords passed to the matplotlib.pyplot.scatter call.

line_kwargsdict, default=None

Dictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.

Returns:

displayPredictionErrorDisplay

Object that stores computed values.

Examples using sklearn.metrics.PredictionErrorDisplay

Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions

Examples using sklearn.metrics.PredictionErrorDisplay.from_estimator

Release Highlights for scikit-learn 1.2

Release Highlights for scikit-learn 1.2

Examples using sklearn.metrics.PredictionErrorDisplay.from_predictions

Combine predictors using stacking

Combine predictors using stacking

Time-related feature engineering

Time-related feature engineering

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

Plotting Cross-Validated Predictions

Plotting Cross-Validated Predictions

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model