OneVsOneClassifier
One-vs-one multiclass strategy.
This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n\_classes \* (n\_classes \- 1) / 2 classifiers, this method is usually slower than one-vs-the-rest, due to its O(n_classes^2) complexity. However, this method may be advantageous for algorithms such as kernel algorithms which don’t scale well with n\_samples. This is because each individual learning problem only involves a small subset of the data whereas, with one-vs-the-rest, the complete dataset is used n\_classes times.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new OneVsOneClassifier(opts?: object): OneVsOneClassifier;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.estimator? | any | A regressor or a classifier that implements fit. When a classifier is passed, decision_function will be used in priority and it will fallback to predict_proba if it is not available. When a regressor is passed, predict is used. |
opts.n_jobs? | number | The number of jobs to use for the computation: the n\_classes \* ( n\_classes \- 1) / 2 OVO problems are computed in parallel. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. See Glossary for more details. |
Returns
Defined in: generated/multiclass/OneVsOneClassifier.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/multiclass/OneVsOneClassifier.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/multiclass/OneVsOneClassifier.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/multiclass/OneVsOneClassifier.ts:21 (opens in a new tab)
id
string
Defined in: generated/multiclass/OneVsOneClassifier.ts:18 (opens in a new tab)
opts
any
Defined in: generated/multiclass/OneVsOneClassifier.ts:19 (opens in a new tab)
Accessors
classes_
Array containing labels.
Signature
classes_(): Promise<any>;Returns
Promise<any>
Defined in: generated/multiclass/OneVsOneClassifier.ts:366 (opens in a new tab)
estimators_
Estimators used for predictions.
Signature
estimators_(): Promise<any>;Returns
Promise<any>
Defined in: generated/multiclass/OneVsOneClassifier.ts:339 (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/multiclass/OneVsOneClassifier.ts:447 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/multiclass/OneVsOneClassifier.ts:420 (opens in a new tab)
pairwise_indices_
Indices of samples used when training the estimators. undefined when estimator’s pairwise tag is false.
Signature
pairwise_indices_(): Promise<any[]>;Returns
Promise<any[]>
Defined in: generated/multiclass/OneVsOneClassifier.ts:393 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/multiclass/OneVsOneClassifier.ts:42 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/multiclass/OneVsOneClassifier.ts:46 (opens in a new tab)
Methods
decision_function()
Decision function for the OneVsOneClassifier.
The decision values for the samples are computed by adding the normalized sum of pair-wise classification confidence levels to the votes in order to disambiguate between the decision values when the votes for all the classes are equal leading to a tie.
Signature
decision_function(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Input data. |
Returns
Promise<ArrayLike[]>
Defined in: generated/multiclass/OneVsOneClassifier.ts:118 (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/multiclass/OneVsOneClassifier.ts:99 (opens in a new tab)
fit()
Fit underlying estimators.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Data. |
opts.y? | ArrayLike | Multi-class targets. |
Returns
Promise<any>
Defined in: generated/multiclass/OneVsOneClassifier.ts:156 (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/multiclass/OneVsOneClassifier.ts:55 (opens in a new tab)
partial_fit()
Partially fit underlying estimators.
Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration, where the first call should have an array of all target variables.
Signature
partial_fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any[] | Data. |
opts.classes? | any | Classes across all calls to partial_fit. Can be obtained via np.unique(y\_all), where y_all is the target vector of the entire dataset. This argument is only required in the first call of partial_fit and can be omitted in the subsequent calls. |
opts.y? | ArrayLike | Multi-class targets. |
Returns
Promise<any>
Defined in: generated/multiclass/OneVsOneClassifier.ts:200 (opens in a new tab)
predict()
Estimate the best class label for each sample in X.
This is implemented as argmax(decision\_function(X), axis=1) which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair.
Signature
predict(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Data. |
Returns
Promise<any>
Defined in: generated/multiclass/OneVsOneClassifier.ts:253 (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/multiclass/OneVsOneClassifier.ts:290 (opens in a new tab)