For general information regarding scikit-learn visualization tools, see
the Visualization Guide.
For guidance on interpreting these plots, refer to the Model
Evaluation Guide.
Parameters:
precisionndarray
Precision values.
recallndarray
Recall values.
average_precisionfloat, default=None
Average precision. If None, the average precision is not shown.
estimator_namestr, default=None
Name of estimator. If None, then the estimator name is not shown.
pos_labelint, float, bool or str, default=None
The class considered as the positive class. If None, the class will not
be shown in the legend.
Added in version 0.24.
prevalence_pos_labelfloat, default=None
The prevalence of the positive label. It is used for plotting the
chance level line. If None, the chance level line will not be plotted
even if plot_chance_level is set to True when plotting.
Added in version 1.3.
Attributes:
line_matplotlib Artist
Precision recall curve.
chance_level_matplotlib Artist or None
The chance level line. It is None if the chance level is not plotted.
Plot Precision Recall Curve using predictions from a binary classifier.
Notes
The average precision (cf. average_precision_score) in
scikit-learn is computed without any interpolation. To be consistent with
this metric, the precision-recall curve is plotted without any
interpolation as well (step-wise style).
You can change this style by passing the keyword argument
drawstyle="default" in plot, from_estimator, or
from_predictions. However, the curve will not be strictly
consistent with the reported average precision.
Plot precision-recall curve given an estimator and some data.
For general information regarding scikit-learn visualization tools, see
the Visualization Guide.
For guidance on interpreting these plots, refer to the Model
Evaluation Guide.
Parameters:
estimatorestimator instance
Fitted classifier or a fitted Pipeline
in which the last estimator is a classifier.
X{array-like, sparse matrix} of shape (n_samples, n_features)
Input values.
yarray-like of shape (n_samples,)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
drop_intermediatebool, default=False
Whether to drop some suboptimal thresholds which would not appear
on a plotted precision-recall curve. This is useful in order to
create lighter precision-recall curves.
The class considered as the positive class when computing the
precision and recall metrics. By default, estimators.classes_[1]
is considered as the positive class.
namestr, default=None
Name for labeling curve. If None, no name is used.
axmatplotlib axes, default=None
Axes object to plot on. If None, a new figure and axes is created.
plot_chance_levelbool, default=False
Whether to plot the chance level. The chance level is the prevalence
of the positive label computed from the data passed during
from_estimator or from_predictions call.
Added in version 1.3.
chance_level_kwdict, default=None
Keyword arguments to be passed to matplotlib’s plot for rendering
the chance level line.
Added in version 1.3.
despinebool, default=False
Whether to remove the top and right spines from the plot.
Added in version 1.6.
**kwargsdict
Keyword arguments to be passed to matplotlib’s plot.
Plot precision-recall curve using estimated probabilities or output of decision function.
Notes
The average precision (cf. average_precision_score)
in scikit-learn is computed without any interpolation. To be consistent
with this metric, the precision-recall curve is plotted without any
interpolation as well (step-wise style).
You can change this style by passing the keyword argument
drawstyle="default". However, the curve will not be strictly
consistent with the reported average precision.
Plot precision-recall curve given binary class predictions.
For general information regarding scikit-learn visualization tools, see
the Visualization Guide.
For guidance on interpreting these plots, refer to the Model
Evaluation Guide.
Parameters:
y_truearray-like of shape (n_samples,)
True binary labels.
y_predarray-like of shape (n_samples,)
Estimated probabilities or output of decision function.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
drop_intermediatebool, default=False
Whether to drop some suboptimal thresholds which would not appear
on a plotted precision-recall curve. This is useful in order to
create lighter precision-recall curves.
Added in version 1.3.
pos_labelint, float, bool or str, default=None
The class considered as the positive class when computing the
precision and recall metrics.
namestr, default=None
Name for labeling curve. If None, name will be set to
"Classifier".
axmatplotlib axes, default=None
Axes object to plot on. If None, a new figure and axes is created.
plot_chance_levelbool, default=False
Whether to plot the chance level. The chance level is the prevalence
of the positive label computed from the data passed during
from_estimator or from_predictions call.
Added in version 1.3.
chance_level_kwdict, default=None
Keyword arguments to be passed to matplotlib’s plot for rendering
the chance level line.
Added in version 1.3.
despinebool, default=False
Whether to remove the top and right spines from the plot.
Added in version 1.6.
**kwargsdict
Keyword arguments to be passed to matplotlib’s plot.
The average precision (cf. average_precision_score)
in scikit-learn is computed without any interpolation. To be consistent
with this metric, the precision-recall curve is plotted without any
interpolation as well (step-wise style).
You can change this style by passing the keyword argument
drawstyle="default". However, the curve will not be strictly
consistent with the reported average precision.
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.
namestr, default=None
Name of precision recall curve for labeling. If None, use
estimator_name if not None, otherwise no labeling is shown.
plot_chance_levelbool, default=False
Whether to plot the chance level. The chance level is the prevalence
of the positive label computed from the data passed during
from_estimator or from_predictions call.
Added in version 1.3.
chance_level_kwdict, default=None
Keyword arguments to be passed to matplotlib’s plot for rendering
the chance level line.
Added in version 1.3.
despinebool, default=False
Whether to remove the top and right spines from the plot.
Added in version 1.6.
**kwargsdict
Keyword arguments to be passed to matplotlib’s plot.
The average precision (cf. average_precision_score)
in scikit-learn is computed without any interpolation. To be consistent
with this metric, the precision-recall curve is plotted without any
interpolation as well (step-wise style).
You can change this style by passing the keyword argument
drawstyle="default". However, the curve will not be strictly
consistent with the reported average precision.