skpro.utils.plotting.plot_crossplot_interval

skpro.utils.plotting.plot_crossplot_interval(y_true, y_pred, coverage=None, ax=None)

Probabilistic cross-plot for regression, truth vs prediction interval.

Plots:

• x-axis: ground truth value

• y-axis: median predictive value, with error bars being the prediction interval at symmetric coverage coverage

Parameters:

y_truearray-like, [n_samples, n_targets]

Ground truth values

y_predskpro distribution, or predict_interval return, [n_samples, n_targets]

symmetric prediction intervals are obtained via the coverage parameterfrom y_pred Predicted values

coveragefloat, optional, default=0.9

Coverage of the prediction interval Used only if y_pred a distribution

axmatplotlib axes, optional

Axes to plot on, if None, a new figure is created and returned

Returns:

axmatplotlib axes

Axes containing the plot If ax was None, a new figure is created and returned If ax was not None, the same ax is returned with plot added

Examples

>>> from skpro.utils.plotting import plot_crossplot_interval  
>>> from skpro.regression.residual import ResidualDouble  
>>> from sklearn.ensemble import RandomForestRegressor  
>>> from sklearn.linear_model import LinearRegression  
>>> from sklearn.datasets import load_diabetes  
>>>
>>> X, y = load_diabetes(return_X_y=True, as_frame=True)  
>>> reg_mean = LinearRegression()  
>>> reg_resid = RandomForestRegressor()  
>>> reg_proba = ResidualDouble(reg_mean, reg_resid)  
>>>
>>> reg_proba.fit(X, y)  
ResidualDouble(...)
>>> y_pred = reg_proba.predict_proba(X)  
>>> plot_crossplot_interval(y, y_pred)