MultiOutputRegressor
Multi target regression.
This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new MultiOutputRegressor(opts?: object): MultiOutputRegressor;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.estimator? | any | An estimator object implementing fit and predict. |
opts.n_jobs? | number | The number of jobs to run in parallel. fit, predict and partial\_fit (if supported by the passed estimator) will be parallelized for each target. When individual estimators are fast to train or predict, using n\_jobs > 1 can result in slower performance due to the parallelism overhead. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all available processes / threads. See Glossary for more details. |
Returns
Defined in: generated/multioutput/MultiOutputRegressor.ts:23 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/multioutput/MultiOutputRegressor.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/multioutput/MultiOutputRegressor.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/multioutput/MultiOutputRegressor.ts:19 (opens in a new tab)
id
string
Defined in: generated/multioutput/MultiOutputRegressor.ts:16 (opens in a new tab)
opts
any
Defined in: generated/multioutput/MultiOutputRegressor.ts:17 (opens in a new tab)
Accessors
estimators_
Estimators used for predictions.
Signature
estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputRegressor.ts:309 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Only defined if the underlying estimators expose such an attribute when fit.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/multioutput/MultiOutputRegressor.ts:363 (opens in a new tab)
n_features_in_
Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/multioutput/MultiOutputRegressor.ts:336 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/multioutput/MultiOutputRegressor.ts:42 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/multioutput/MultiOutputRegressor.ts:46 (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/multioutput/MultiOutputRegressor.ts:99 (opens in a new tab)
fit()
Fit the model to data, separately for each output variable.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
opts.fit_params? | any | Parameters passed to the estimator.fit method of each step. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. Only supported if the underlying regressor supports sample weights. |
opts.y? | ArrayLike | Multi-output targets. An indicator matrix turns on multilabel estimation. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputRegressor.ts:116 (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/multioutput/MultiOutputRegressor.ts:55 (opens in a new tab)
partial_fit()
Incrementally fit the model to data, for each output variable.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. Only supported if the underlying regressor supports sample weights. |
opts.y? | ArrayLike | Multi-output targets. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputRegressor.ts:172 (opens in a new tab)
predict()
Predict multi-output variable using model for each target variable.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
Returns
Promise<ArrayLike>
Defined in: generated/multioutput/MultiOutputRegressor.ts:223 (opens in a new tab)
score()
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n\_samples, n\_samples\_fitted), where n\_samples\_fitted is the number of samples used in the fitting for the estimator. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/multioutput/MultiOutputRegressor.ts:260 (opens in a new tab)