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 | 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
Defined in: generated/calibration/CalibrationDisplay.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/calibration/CalibrationDisplay.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/calibration/CalibrationDisplay.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/calibration/CalibrationDisplay.ts:21 (opens in a new tab)
id
string
Defined in: generated/calibration/CalibrationDisplay.ts:18 (opens in a new tab)
opts
any
Defined in: generated/calibration/CalibrationDisplay.ts:19 (opens in a new tab)
Accessors
ax_
Axes with calibration curve.
Signature
ax_(): Promise<any>;Returns
Promise<any>
Defined in: generated/calibration/CalibrationDisplay.ts:426 (opens in a new tab)
figure_
Figure containing the curve.
Signature
figure_(): Promise<any>;Returns
Promise<any>
Defined in: generated/calibration/CalibrationDisplay.ts:453 (opens in a new tab)
line_
Calibration curve.
Signature
line_(): Promise<any>;Returns
Promise<any>
Defined in: generated/calibration/CalibrationDisplay.ts:399 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/calibration/CalibrationDisplay.ts:55 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/calibration/CalibrationDisplay.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/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 | 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 | 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 |
Returns
Promise<any>
Defined in: generated/calibration/CalibrationDisplay.ts:345 (opens in a new tab)