PredictionErrorDisplay

Visualization of the prediction error of a regression model.

This tool can display “residuals vs predicted” or “actual vs predicted” using scatter plots to qualitatively assess the behavior of a regressor, preferably on held-out data points.

See the details in the docstrings of from\_estimator or from\_predictions to create a visualizer. All parameters are stored as attributes.

For general information regarding scikit-learn visualization tools, read more in the Visualization Guide. For details regarding interpreting these plots, refer to the Model Evaluation Guide.

Python Reference (opens in a new tab)

Constructors

constructor()

Signature

new PredictionErrorDisplay(opts?: object): PredictionErrorDisplay;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.y_pred?`	`ArrayLike`	Prediction values.
`opts.y_true?`	`ArrayLike`	True values.

Returns

PredictionErrorDisplay

Defined in: generated/metrics/PredictionErrorDisplay.ts:27 (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/PredictionErrorDisplay.ts:101 (opens in a new tab)

from_estimator()

Plot the prediction error given a regressor and some data.

For general information regarding scikit-learn visualization tools, read more in the Visualization Guide. For details regarding interpreting these plots, refer to the Model Evaluation Guide.

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 regressor or a fitted `Pipeline` in which the last estimator is a regressor.
`opts.kind?`	`"actual_vs_predicted"` \| `"residual_vs_predicted"`	The type of plot to draw: `Default Value` `'residual_vs_predicted'`
`opts.line_kwargs?`	`any`	Dictionary with keyword passed to the `matplotlib.pyplot.plot` call to draw the optimal line.
`opts.random_state?`	`number`	Controls the randomness when `subsample` is not `undefined`. See Glossary for details.
`opts.scatter_kwargs?`	`any`	Dictionary with keywords passed to the `matplotlib.pyplot.scatter` call.
`opts.subsample?`	`number`	Sampling the samples to be shown on the scatter plot. If `float`, it should be between 0 and 1 and represents the proportion of the original dataset. If `int`, it represents the number of samples display on the scatter plot. If `undefined`, no subsampling will be applied. by default, a 1000 samples or less will be displayed. `Default Value` `1`
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:120 (opens in a new tab)

from_predictions()

Plot the prediction error given the true and predicted targets.

For general information regarding scikit-learn visualization tools, read more in the Visualization Guide. For details regarding interpreting these plots, refer to the Model Evaluation Guide.

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.kind?`	`"actual_vs_predicted"` \| `"residual_vs_predicted"`	The type of plot to draw: `Default Value` `'residual_vs_predicted'`
`opts.line_kwargs?`	`any`	Dictionary with keyword passed to the `matplotlib.pyplot.plot` call to draw the optimal line.
`opts.random_state?`	`number`	Controls the randomness when `subsample` is not `undefined`. See Glossary for details.
`opts.scatter_kwargs?`	`any`	Dictionary with keywords passed to the `matplotlib.pyplot.scatter` call.
`opts.subsample?`	`number`	Sampling the samples to be shown on the scatter plot. If `float`, it should be between 0 and 1 and represents the proportion of the original dataset. If `int`, it represents the number of samples display on the scatter plot. If `undefined`, no subsampling will be applied. by default, a 1000 samples or less will be displayed. `Default Value` `1`
`opts.y_pred?`	`ArrayLike`	Predicted target values.
`opts.y_true?`	`ArrayLike`	True target values.

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:214 (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/PredictionErrorDisplay.ts:55 (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.kind?`	`"actual_vs_predicted"` \| `"residual_vs_predicted"`	The type of plot to draw: `Default Value` `'residual_vs_predicted'`
`opts.line_kwargs?`	`any`	Dictionary with keyword passed to the `matplotlib.pyplot.plot` call to draw the optimal line.
`opts.scatter_kwargs?`	`any`	Dictionary with keywords passed to the `matplotlib.pyplot.scatter` call.

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:301 (opens in a new tab)

Properties

_isDisposed

boolean = false

Defined in: generated/metrics/PredictionErrorDisplay.ts:25 (opens in a new tab)

_isInitialized

boolean = false

Defined in: generated/metrics/PredictionErrorDisplay.ts:24 (opens in a new tab)

_py

PythonBridge

Defined in: generated/metrics/PredictionErrorDisplay.ts:23 (opens in a new tab)

id

string

Defined in: generated/metrics/PredictionErrorDisplay.ts:20 (opens in a new tab)

opts

any

Defined in: generated/metrics/PredictionErrorDisplay.ts:21 (opens in a new tab)

Accessors

ax_

Axes with the different matplotlib axis.

Signature

ax_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:436 (opens in a new tab)

errors_lines_

Residual lines. If with\_errors=False, then it is set to undefined.

Signature

errors_lines_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:382 (opens in a new tab)

figure_

Figure containing the scatter and lines.

Signature

figure_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:463 (opens in a new tab)

line_

Optimal line representing y\_true \== y\_pred. Therefore, it is a diagonal line for kind="predictions" and a horizontal line for kind="residuals".