ClassifierChain
A multi-label model that arranges binary classifiers 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 ClassifierChain(opts?: object): ClassifierChain;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.base_estimator? | any | The base estimator from which the classifier 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/ClassifierChain.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/multioutput/ClassifierChain.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/multioutput/ClassifierChain.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/multioutput/ClassifierChain.ts:21 (opens in a new tab)
id
string
Defined in: generated/multioutput/ClassifierChain.ts:18 (opens in a new tab)
opts
any
Defined in: generated/multioutput/ClassifierChain.ts:19 (opens in a new tab)
Accessors
classes_
A list of arrays of length len(estimators\_) containing the class labels for each estimator in the chain.
Signature
classes_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/multioutput/ClassifierChain.ts:321 (opens in a new tab)
estimators_
A list of clones of base_estimator.
Signature
estimators_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/multioutput/ClassifierChain.ts:346 (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/ClassifierChain.ts:421 (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/ClassifierChain.ts:396 (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/ClassifierChain.ts:371 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/multioutput/ClassifierChain.ts:57 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/multioutput/ClassifierChain.ts:61 (opens in a new tab)
Methods
decision_function()
Evaluate the decision_function of the models in the chain.
Signature
decision_function(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The input data. |
Returns
Promise<ArrayLike[]>
Defined in: generated/multioutput/ClassifierChain.ts:131 (opens in a new tab)
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/ClassifierChain.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. |
Returns
Promise<any>
Defined in: generated/multioutput/ClassifierChain.ts:166 (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/ClassifierChain.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/ClassifierChain.ts:206 (opens in a new tab)
predict_proba()
Predict probability estimates.
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The input data. |
Returns
Promise<ArrayLike[]>
Defined in: generated/multioutput/ClassifierChain.ts:239 (opens in a new tab)
score()
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/multioutput/ClassifierChain.ts:274 (opens in a new tab)