Class: DummyRegressor
Regressor that makes predictions using simple rules.
This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems.
Read more in the User Guide.
Constructors
new DummyRegressor()
new DummyRegressor(
opts?):DummyRegressor
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.constant? | number | ArrayLike | The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy. |
opts.quantile? | number | The quantile to predict using the “quantile” strategy. A quantile of 0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the maximum. |
opts.strategy? | "quantile" | "constant" | "mean" | "median" | Strategy to use to generate predictions. |
Returns DummyRegressor
Defined in generated/dummy/DummyRegressor.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/dummy/DummyRegressor.ts:23 |
_isInitialized | boolean | false | generated/dummy/DummyRegressor.ts:22 |
_py | PythonBridge | undefined | generated/dummy/DummyRegressor.ts:21 |
id | string | undefined | generated/dummy/DummyRegressor.ts:18 |
opts | any | undefined | generated/dummy/DummyRegressor.ts:19 |
Accessors
constant_
Get Signature
get constant_():
Promise<ArrayLike[]>
Mean or median or quantile of the training targets or constant value given by the user.
Returns Promise<ArrayLike[]>
Defined in generated/dummy/DummyRegressor.ts:391
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/dummy/DummyRegressor.ts:441
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/dummy/DummyRegressor.ts:416
n_outputs_
Get Signature
get n_outputs_():
Promise<number>
Number of outputs.
Returns Promise<number>
Defined in generated/dummy/DummyRegressor.ts:466
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/dummy/DummyRegressor.ts:47
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/dummy/DummyRegressor.ts:99
fit()
fit(
opts):Promise<any>
Fit the random regressor.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Training data. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/dummy/DummyRegressor.ts:116
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/dummy/DummyRegressor.ts:160
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/dummy/DummyRegressor.ts:60
predict()
predict(
opts):Promise<ArrayLike>
Perform classification on test vectors X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.return_std? | boolean | Whether to return the standard deviation of posterior prediction. All zeros in this case. |
opts.X? | ArrayLike[] | Test data. |
Returns Promise<ArrayLike>
Defined in generated/dummy/DummyRegressor.ts:194
score()
score(
opts):Promise<number>
Return the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 \- u/v), where u is the residual sum of squares ((y_true \- y_pred) \*\* 2).sum() and v is the total sum of squares ((y_true \- y_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. Passing undefined as test samples gives the same result as passing real test samples, since DummyRegressor operates independently of the sampled observations. |
opts.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/dummy/DummyRegressor.ts:235
set_fit_request()
set_fit_request(
opts):Promise<any>
Request metadata passed to the fit method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in fit. |
Returns Promise<any>
Defined in generated/dummy/DummyRegressor.ts:281
set_predict_request()
set_predict_request(
opts):Promise<any>
Request metadata passed to the predict method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.return_std? | string | boolean | Metadata routing for return_std parameter in predict. |
Returns Promise<any>
Defined in generated/dummy/DummyRegressor.ts:319
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/dummy/DummyRegressor.ts:357