PrecisionRecallDisplay
Precision Recall visualization.
It is recommend to use from\_estimator or from\_predictions to create a PredictionRecallDisplay. All parameters are stored as attributes.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new PrecisionRecallDisplay(opts?: object): PrecisionRecallDisplay;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.average_precision? | number | Average precision. If undefined, the average precision is not shown. |
opts.estimator_name? | string | Name of estimator. If undefined, then the estimator name is not shown. |
opts.pos_label? | string | number | The class considered as the positive class. If undefined, the class will not be shown in the legend. |
opts.precision? | ArrayLike | Precision values. |
opts.recall? | ArrayLike | Recall values. |
Returns
Defined in: generated/metrics/PrecisionRecallDisplay.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/metrics/PrecisionRecallDisplay.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/metrics/PrecisionRecallDisplay.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/metrics/PrecisionRecallDisplay.ts:21 (opens in a new tab)
id
string
Defined in: generated/metrics/PrecisionRecallDisplay.ts:18 (opens in a new tab)
opts
any
Defined in: generated/metrics/PrecisionRecallDisplay.ts:19 (opens in a new tab)
Accessors
ax_
Axes with precision recall curve.
Signature
ax_(): Promise<any>;Returns
Promise<any>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:383 (opens in a new tab)
figure_
Figure containing the curve.
Signature
figure_(): Promise<any>;Returns
Promise<any>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:410 (opens in a new tab)
line_
Precision recall curve.
Signature
line_(): Promise<any>;Returns
Promise<any>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:356 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/metrics/PrecisionRecallDisplay.ts:55 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/metrics/PrecisionRecallDisplay.ts:59 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:120 (opens in a new tab)
from_estimator()
Plot precision-recall curve given an estimator and some data.
Signature
from_estimator(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Input values. |
opts.ax? | any | Axes object to plot on. If undefined, a new figure and axes is created. |
opts.estimator? | any | Fitted classifier or a fitted Pipeline in which the last estimator is a classifier. |
opts.kwargs? | any | Keyword arguments to be passed to matplotlib’s plot. |
opts.name? | string | Name for labeling curve. If undefined, no name is used. |
opts.pos_label? | string | number | 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. |
opts.response_method? | "auto" | "predict_proba" | "decision_function" | Specifies whether to use predict_proba or decision_function as the target response. If set to ‘auto’, predict_proba is tried first and if it does not exist decision_function is tried next. Default Value 'auto' |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:137 (opens in a new tab)
from_predictions()
Plot precision-recall curve given binary class predictions.
Signature
from_predictions(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.ax? | any | Axes object to plot on. If undefined, a new figure and axes is created. |
opts.kwargs? | any | Keyword arguments to be passed to matplotlib’s plot. |
opts.name? | string | Name for labeling curve. If undefined, name will be set to "Classifier". |
opts.pos_label? | string | number | The class considered as the positive class when computing the precision and recall metrics. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y_pred? | ArrayLike | Estimated probabilities or output of decision function. |
opts.y_true? | ArrayLike | True binary labels. |
Returns
Promise<any>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:229 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:68 (opens in a new tab)
plot()
Plot visualization.
Extra keyword arguments will be passed to matplotlib’s plot.
Signature
plot(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.ax? | any | Axes object to plot on. If undefined, a new figure and axes is created. |
opts.kwargs? | any | Keyword arguments to be passed to matplotlib’s plot. |
opts.name? | string | Name of precision recall curve for labeling. If undefined, use estimator\_name if not undefined, otherwise no labeling is shown. |
Returns
Promise<any>
Defined in: generated/metrics/PrecisionRecallDisplay.ts:309 (opens in a new tab)