Class: RocCurveDisplay

ROC Curve visualization.

It is recommend to use from_estimator or from_predictions to create a RocCurveDisplay. All parameters are stored as attributes.

Constructors

new RocCurveDisplay()

new RocCurveDisplay(opts?): RocCurveDisplay

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.estimator_name`?	`string`	Name of estimator. If `undefined`, the estimator name is not shown.
`opts.fpr`?	`ArrayLike`	False positive rate.
`opts.pos_label`?	`string` \| `number` \| `boolean`	The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes_\[1\]` is considered as the positive class.
`opts.roc_auc`?	`number`	Area under ROC curve. If `undefined`, the roc_auc score is not shown.
`opts.tpr`?	`ArrayLike`	True positive rate.

Returns RocCurveDisplay

Defined in generated/metrics/RocCurveDisplay.ts:25

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/metrics/RocCurveDisplay.ts:23
`_isInitialized`	`boolean`	`false`	generated/metrics/RocCurveDisplay.ts:22
`_py`	`PythonBridge`	`undefined`	generated/metrics/RocCurveDisplay.ts:21
`id`	`string`	`undefined`	generated/metrics/RocCurveDisplay.ts:18
`opts`	`any`	`undefined`	generated/metrics/RocCurveDisplay.ts:19

Accessors

ax_

Get Signature

get ax_(): Promise<any>

Axes with ROC Curve.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:406

chance_level_

Get Signature

get chance_level_(): Promise<any>

The chance level line. It is undefined if the chance level is not plotted.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:381

figure_

Get Signature

get figure_(): Promise<any>

Figure containing the curve.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:429

line_

Get Signature

get line_(): Promise<any>

ROC Curve.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:358

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/metrics/RocCurveDisplay.ts:55

Methods

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/metrics/RocCurveDisplay.ts:107

from_estimator()

from_estimator(opts): Promise<any>

Create a ROC Curve display from an estimator.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.ax`?	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.chance_level_kw`?	`any`	Keyword arguments to be passed to matplotlib’s `plot` for rendering the chance level line.
`opts.drop_intermediate`?	`boolean`	Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.
`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 of ROC Curve for labeling. If `undefined`, use the name of the estimator.
`opts.plot_chance_level`?	`boolean`	Whether to plot the chance level.
`opts.pos_label`?	`string` \| `number` \| `boolean`	The class considered as the positive class when computing the roc auc metrics. By default, `estimators.classes_\[1\]` is considered as the positive class.
`opts.response_method`?	`"decision_function"` \| `"auto’} default=’auto"`	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.
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.X`?	`ArrayLike`	Input values.
`opts.y`?	`ArrayLike`	Target values.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:124

from_predictions()

from_predictions(opts): Promise<any>

Plot ROC curve given the true and predicted values.

Read more in the User Guide.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.ax`?	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.chance_level_kw`?	`any`	Keyword arguments to be passed to matplotlib’s `plot` for rendering the chance level line.
`opts.drop_intermediate`?	`boolean`	Whether to drop some suboptimal thresholds which would not appear on a plotted ROC curve. This is useful in order to create lighter ROC curves.
`opts.kwargs`?	`any`	Additional keywords arguments passed to matplotlib `plot` function.
`opts.name`?	`string`	Name of ROC curve for labeling. If `undefined`, name will be set to `"Classifier"`.
`opts.plot_chance_level`?	`boolean`	Whether to plot the chance level.
`opts.pos_label`?	`string` \| `number` \| `boolean`	The label of the positive class. When `pos_label=None`, if `y_true` is in {-1, 1} or {0, 1}, `pos_label` is set to 1, otherwise an error will be raised.
`opts.sample_weight`?	`ArrayLike`	Sample weights.
`opts.y_pred`?	`ArrayLike`	Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by “decision_function” on some classifiers).
`opts.y_true`?	`ArrayLike`	True labels.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:219

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/metrics/RocCurveDisplay.ts:68

plot()

plot(opts): Promise<any>

Plot visualization.

Extra keyword arguments will be passed to matplotlib’s plot.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.ax`?	`any`	Axes object to plot on. If `undefined`, a new figure and axes is created.
`opts.chance_level_kw`?	`any`	Keyword arguments to be passed to matplotlib’s `plot` for rendering the chance level line.
`opts.kwargs`?	`any`	Keyword arguments to be passed to matplotlib’s `plot`.
`opts.name`?	`string`	Name of ROC Curve for labeling. If `undefined`, use `estimator_name` if not `undefined`, otherwise no labeling is shown.
`opts.plot_chance_level`?	`boolean`	Whether to plot the chance level.

Returns Promise<any>

Defined in generated/metrics/RocCurveDisplay.ts:304

RobustScaler SelectFdr