package sklearn
Module Metrics.PrecisionRecallDisplaySource
val create :
?average_precision:float ->
?estimator_name:string ->
precision:[> `ArrayLike ] Np.Obj.t ->
recall:[> `ArrayLike ] Np.Obj.t ->
unit ->
tPrecision Recall visualization.
It is recommend to use :func:`~sklearn.metrics.plot_precision_recall_curve` to create a visualizer. All parameters are stored as attributes.
Read more in the :ref:`User Guide <visualizations>`.
Parameters ----------- precision : ndarray Precision values.
recall : ndarray Recall values.
average_precision : float, default=None Average precision. If None, the average precision is not shown.
estimator_name : str, default=None Name of estimator. If None, then the estimator name is not shown.
Attributes ---------- line_ : matplotlib Artist Precision recall curve.
ax_ : matplotlib Axes Axes with precision recall curve.
figure_ : matplotlib Figure Figure containing the curve.
val plot :
?ax:Py.Object.t ->
?name:string ->
?kwargs:(string * Py.Object.t) list ->
[> tag ] Obj.t ->
Py.Object.tPlot visualization.
Extra keyword arguments will be passed to matplotlib's `plot`.
Parameters ---------- ax : Matplotlib Axes, default=None Axes object to plot on. If `None`, a new figure and axes is created.
name : str, default=None Name of precision recall curve for labeling. If `None`, use the name of the estimator.
**kwargs : dict Keyword arguments to be passed to matplotlib's `plot`.
Returns ------- display : :class:`~sklearn.metrics.PrecisionRecallDisplay` Object that stores computed values.
Attribute line_: get value or raise Not_found if None.
Attribute line_: get value as an option.
Attribute ax_: get value or raise Not_found if None.
Attribute ax_: get value as an option.
Attribute figure_: get value or raise Not_found if None.
Attribute figure_: get value as an option.
Pretty-print the object to a formatter.