# OneVsOneClassifier

### *class* cuml.multiclass.OneVsOneClassifier(estimator, , verbose=False, output_type=None)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/multiclass/multiclass.py#L238)

Fit one binary classifier per pair of classes. The input can be any kind
of cuML compatible array, and the output type follows cuML’s output type
configuration rules.

The input is converted to a host (NumPy) array and partitioned into binary
classification problems. Each cuML estimator transforms its partition
back to the device. These host/device copies have some overhead. For more
details see issue [https://github.com/NVIDIA/cuml/issues/2876](https://github.com/NVIDIA/cuml/issues/2876).

For documentation see [scikit-learn’s OneVsOneClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsOneClassifier.html).

* **Parameters:**
  **estimator**

  **verbose**
  : Sets logging level. It must be one of `cuml.common.logger.level_*`.
    See [Verbosity Levels](../../cuml/index.html.md#verbosity-levels) for more info.

  **output_type**
  : Return results and set estimator attributes to the indicated output
    type. If None, the output type set at the module level
    (`cuml.global_settings.output_type`) will be used. See
    [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration) for more info.

### Examples

```pycon
>>> from cuml.linear_model import LogisticRegression
>>> from cuml.multiclass import OneVsOneClassifier
>>> from cuml.datasets.classification import make_classification
```

```pycon
>>> X, y = make_classification(n_samples=10, n_features=6,
...                            n_informative=4, n_classes=3,
...                            random_state=137)
```

```pycon
>>> cls = OneVsOneClassifier(LogisticRegression())
>>> cls.fit(X, y)
OneVsOneClassifier(estimator=LogisticRegression())
>>> cls.predict(X)
array([1, 1, 0, 1, 1, 1, 2, 2, 1, 2])
```

<!-- !! processed by numpydoc !! -->

#### decision_function(X)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/multiclass/multiclass.py#L168)

Calculate the decision function.

* **Parameters:**
  **X**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **results**
  : Decision function values
    <br/>
    For more information on how to configure cuML’s output type,
    refer to: [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration).

<!-- !! processed by numpydoc !! -->

#### fit(X, y, sample_weight=None) → \_BaseMulticlassClassifier[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/multiclass/multiclass.py#L96)

Fit a multiclass classifier.

* **Parameters:**
  **X**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

  **y**
  : Dense matrix with any dtype.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

  **sample_weight**
  : The weights for each observation in X. If None, all observations
    are assigned equal weight.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

<!-- !! processed by numpydoc !! -->

#### get_params(deep=True)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L191)

Returns a dict of all params owned by this class. If the child class
has appropriately overridden the `_get_param_names` method and does not
need anything other than what is there in this method, then it doesn’t
have to override this method

<!-- !! processed by numpydoc !! -->

#### predict(X)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/multiclass/multiclass.py#L144)

Predict using multi class classifier.

* **Parameters:**
  **X**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **preds**
  : Predicted values
    <br/>
    For more information on how to configure cuML’s output type,
    refer to: [Output Data Type Configuration](../../cuml/index.html.md#output-data-type-configuration).

<!-- !! processed by numpydoc !! -->

#### score(X, y, sample_weight=None, \*\*kwargs)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/mixins.py#L101)

Scoring function for classifier estimators based on mean accuracy.

* **Parameters:**
  **X**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

  **y**
  : Dense matrix with dtype float32 or float64.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.

  **sample_weight**
  : The weights for each observation in X. If None, all observations
    are assigned equal weight.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
* **Returns:**
  **score**
  : Accuracy of self.predict(X) wrt. y (fraction where y == pred_y)

<!-- !! processed by numpydoc !! -->

#### set_params(\*\*params)[[source]](https://github.com/NVIDIA/cuml/blob/93418baf5/python/cuml/cuml/internals/base.py#L200)

Accepts a dict of params and updates the corresponding ones owned by
this class. If the child class has appropriately overridden the
`_get_param_names` method and does not need anything other than what is,
there in this method, then it doesn’t have to override this method

<!-- !! processed by numpydoc !! -->