RegressorChain
A multi-label model that arranges regressions into a chain.
Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predictions of models that are earlier in the chain.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RegressorChain(opts?: object): RegressorChain;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.base_estimator? | any | The base estimator from which the regressor chain is built. |
opts.cv? | number | Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. Possible inputs for cv are: |
opts.order? | ArrayLike | "random" | If undefined, the order will be determined by the order of columns in the label matrix Y.: |
opts.random_state? | number | If order='random', determines random number generation for the chain order. In addition, it controls the random seed given at each base\_estimator at each chaining iteration. Thus, it is only used when base\_estimator exposes a random\_state. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.verbose? | boolean | If true, chain progress is output as each model is completed. Default Value false |
Returns
Defined in: generated/multioutput/RegressorChain.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/multioutput/RegressorChain.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/multioutput/RegressorChain.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/multioutput/RegressorChain.ts:21 (opens in a new tab)
id
string
Defined in: generated/multioutput/RegressorChain.ts:18 (opens in a new tab)
opts
any
Defined in: generated/multioutput/RegressorChain.ts:19 (opens in a new tab)
Accessors
estimators_
A list of clones of base_estimator.
Signature
estimators_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/multioutput/RegressorChain.ts:260 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/multioutput/RegressorChain.ts:333 (opens in a new tab)
n_features_in_
Number of features seen during fit. Only defined if the underlying base\_estimator exposes such an attribute when fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/multioutput/RegressorChain.ts:308 (opens in a new tab)
order_
The order of labels in the classifier chain.
Signature
order_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/multioutput/RegressorChain.ts:285 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/multioutput/RegressorChain.ts:57 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/multioutput/RegressorChain.ts:61 (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/RegressorChain.ts:114 (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 fit method at each step of the regressor chain. |
Returns
Promise<any>
Defined in: generated/multioutput/RegressorChain.ts:131 (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/RegressorChain.ts:70 (opens in a new tab)
predict()
Predict on the data matrix X using the ClassifierChain model.
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/RegressorChain.ts:178 (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/RegressorChain.ts:213 (opens in a new tab)