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Classes
PredictionErrorDisplay

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

NameTypeDescription
opts?object-
opts.y_pred?ArrayLikePrediction values.
opts.y_true?ArrayLikeTrue values.

Returns

PredictionErrorDisplay

Defined in: generated/metrics/PredictionErrorDisplay.ts:27 (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".

Signature

line_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:355 (opens in a new tab)

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/metrics/PredictionErrorDisplay.ts:42 (opens in a new tab)

Signature

py(pythonBridge: PythonBridge): void;

Parameters

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/metrics/PredictionErrorDisplay.ts:46 (opens in a new tab)

scatter_

Scatter data points.

Signature

scatter_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/metrics/PredictionErrorDisplay.ts:409 (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

NameTypeDescription
optsobject-
opts.X?ArrayLikeInput values.
opts.ax?anyAxes object to plot on. If undefined, a new figure and axes is created.
opts.estimator?anyFitted 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?anyDictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.
opts.random_state?numberControls the randomness when subsample is not undefined. See Glossary for details.
opts.scatter_kwargs?anyDictionary with keywords passed to the matplotlib.pyplot.scatter call.
opts.subsample?numberSampling 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?ArrayLikeTarget 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

NameTypeDescription
optsobject-
opts.ax?anyAxes 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?anyDictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.
opts.random_state?numberControls the randomness when subsample is not undefined. See Glossary for details.
opts.scatter_kwargs?anyDictionary with keywords passed to the matplotlib.pyplot.scatter call.
opts.subsample?numberSampling 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?ArrayLikePredicted target values.
opts.y_true?ArrayLikeTrue 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

NameType
pyPythonBridge

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

NameTypeDescription
optsobject-
opts.ax?anyAxes 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?anyDictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.
opts.scatter_kwargs?anyDictionary with keywords passed to the matplotlib.pyplot.scatter call.

Returns

Promise<any>

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