CalibrationDisplay

Calibration curve (also known as reliability diagram) visualization.

It is recommended to use from\_estimator or from\_predictions to create a CalibrationDisplay. All parameters are stored as attributes.

Read more about calibration in the User Guide and more about the scikit-learn visualization API in Visualizations.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new CalibrationDisplay(opts?: object): CalibrationDisplay;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.estimator_name?`	`string`	Name of estimator. If `undefined`, the estimator name is not shown.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The positive class when computing the calibration curve. By default, `estimators.classes\_\[1\]` is considered as the positive class.
`opts.prob_pred?`	`ArrayLike`	The mean predicted probability in each bin.
`opts.prob_true?`	`ArrayLike`	The proportion of samples whose class is the positive class (fraction of positives), in each bin.
`opts.y_prob?`	`ArrayLike`	Probability estimates for the positive class, for each sample.

Returns

CalibrationDisplay

Defined in: generated/calibration/CalibrationDisplay.ts:25 (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/calibration/CalibrationDisplay.ts:122 (opens in a new tab)

from_estimator()

Plot calibration curve using a binary classifier and data.

A calibration curve, also known as a reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis.

Extra keyword arguments will be passed to matplotlib.pyplot.plot (opens in a new tab).

Read more about calibration in the User Guide and more about the scikit-learn visualization API in Visualizations.

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. The classifier must have a predict_proba method.
`opts.kwargs?`	`any`	Keyword arguments to be passed to `matplotlib.pyplot.plot` (opens in a new tab).
`opts.n_bins?`	`number`	Number of bins to discretize the [0, 1] interval into when calculating the calibration curve. A bigger number requires more data. `Default Value` `5`
`opts.name?`	`string`	Name for labeling curve. If `undefined`, the name of the estimator is used.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The positive class when computing the calibration curve. By default, `estimators.classes\_\[1\]` is considered as the positive class.
`opts.ref_line?`	`boolean`	If `true`, plots a reference line representing a perfectly calibrated classifier. `Default Value` `true`
`opts.strategy?`	`"uniform"` \| `"quantile"`	Strategy used to define the widths of the bins. `Default Value` `'uniform'`
`opts.y?`	`ArrayLike`	Binary target values.

Returns

Promise<any>

Defined in: generated/calibration/CalibrationDisplay.ts:145 (opens in a new tab)

from_predictions()

Plot calibration curve using true labels and predicted probabilities.

Calibration curve, also known as reliability diagram, uses inputs from a binary classifier and plots the average predicted probability for each bin against the fraction of positive classes, on the y-axis.

Extra keyword arguments will be passed to matplotlib.pyplot.plot (opens in a new tab).

Read more about calibration in the User Guide and more about the scikit-learn visualization API in Visualizations.

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.pyplot.plot` (opens in a new tab).
`opts.n_bins?`	`number`	Number of bins to discretize the [0, 1] interval into when calculating the calibration curve. A bigger number requires more data. `Default Value` `5`
`opts.name?`	`string`	Name for labeling curve.
`opts.pos_label?`	`string` \| `number` \| `boolean`	The positive class when computing the calibration curve. By default, `estimators.classes\_\[1\]` is considered as the positive class.
`opts.ref_line?`	`boolean`	If `true`, plots a reference line representing a perfectly calibrated classifier. `Default Value` `true`
`opts.strategy?`	`"uniform"` \| `"quantile"`	Strategy used to define the widths of the bins. `Default Value` `'uniform'`
`opts.y_prob?`	`ArrayLike`	The predicted probabilities of the positive class.
`opts.y_true?`	`ArrayLike`	True labels.

Returns

Promise<any>

Defined in: generated/calibration/CalibrationDisplay.ts:249 (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/calibration/CalibrationDisplay.ts:68 (opens in a new tab)

plot()

Plot visualization.

Extra keyword arguments will be passed to matplotlib.pyplot.plot (opens in a new tab).

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.pyplot.plot` (opens in a new tab).
`opts.name?`	`string`	Name for labeling curve. If `undefined`, use `estimator\_name` if not `undefined`, otherwise no labeling is shown.
`opts.ref_line?`	`boolean`	If `true`, plots a reference line representing a perfectly calibrated classifier. `Default Value` `true`