MultiOutputClassifier
Multi target classification.
This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new MultiOutputClassifier(opts?: object): MultiOutputClassifier;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.estimator? | any | An estimator object implementing fit and predict. A predict_proba method will be exposed only if estimator implements it. |
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/MultiOutputClassifier.ts:23 (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/MultiOutputClassifier.ts:99 (opens in a new tab)
fit()
Fit the model to data matrix X and targets Y.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
opts.Y? | ArrayLike[] | The target values. |
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 classifier supports sample weights. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputClassifier.ts:116 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRouter encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputClassifier.ts:174 (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/MultiOutputClassifier.ts:55 (opens in a new tab)
partial_fit()
Incrementally fit a separate model for each class output.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
opts.classes? | any[] | Each array is unique classes for one output in str/int. Can be obtained via \[np.unique(y\[:, i\]) for i in range(y.shape\[1\])\], where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels in classes. |
opts.partial_fit_params? | any | Parameters passed to the estimator.partial\_fit method of each sub-estimator. Only available if enable\_metadata\_routing=True. See the User Guide. |
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/MultiOutputClassifier.ts:212 (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/MultiOutputClassifier.ts:283 (opens in a new tab)
predict_proba()
Return prediction probabilities for each class of each output.
This method will raise a ValueError if any of the estimators do not have predict\_proba.
Signature
predict_proba(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input data. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputClassifier.ts:320 (opens in a new tab)
score()
Return the mean accuracy on the given test data and labels.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.y? | ArrayLike[] | True values for X. |
Returns
Promise<number>
Defined in: generated/multioutput/MultiOutputClassifier.ts:358 (opens in a new tab)
set_fit_request()
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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputClassifier.ts:404 (opens in a new tab)
set_partial_fit_request()
Request metadata passed to the partial\_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:
Signature
set_partial_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.classes? | string | boolean | Metadata routing for classes parameter in partial\_fit. |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in partial\_fit. |
Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputClassifier.ts:446 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/multioutput/MultiOutputClassifier.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/multioutput/MultiOutputClassifier.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/multioutput/MultiOutputClassifier.ts:19 (opens in a new tab)
id
string
Defined in: generated/multioutput/MultiOutputClassifier.ts:16 (opens in a new tab)
opts
any
Defined in: generated/multioutput/MultiOutputClassifier.ts:17 (opens in a new tab)
Accessors
classes_
Class labels.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/multioutput/MultiOutputClassifier.ts:489 (opens in a new tab)
estimators_
Estimators used for predictions.
Signature
estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/multioutput/MultiOutputClassifier.ts:516 (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/MultiOutputClassifier.ts:570 (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/MultiOutputClassifier.ts:543 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/multioutput/MultiOutputClassifier.ts:42 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/multioutput/MultiOutputClassifier.ts:46 (opens in a new tab)