val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
precision:Ndarray.t ->
recall:Ndarray.t ->
average_precision:float ->
estimator_name:string ->
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 Average precision.
estimator_name : str Name of estimator.
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 ->
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.
val line_ : t -> Py.Object.tAttribute line_: see constructor for documentation
val ax_ : t -> Py.Object.tAttribute ax_: see constructor for documentation
val figure_ : t -> Py.Object.tAttribute figure_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.