# `OzaBoost`

### *class* capymoa.classifier.OzaBoost[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/classifier/_oza_boost.py#L12)

Bases: [`MOAClassifier`](capymoa.base.MOAClassifier.md#capymoa.base.MOAClassifier)

Incremental on-line boosting classifier of Oza and Russell.

Incremental on-line boosting classifier of Oza and Russell <sup>[1](#id2)</sup> is a ensemble
classifier. For the boosting method, Oza and Russell note that the weighting
procedure of AdaBoost actually divides the total example weight into two halves –
half of the weight is assigned to the correctly classified examples, and the other
half goes to the misclassified examples. They use the Poisson distribution for
deciding the random probability that an example is used for training, only this time
the parameter changes according to the boosting weight of the example as it is
passed through each model in sequence.

```pycon
>>> from capymoa.classifier import OzaBoost
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = OzaBoost(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results['cumulative'].accuracy():.1f}")
88.8
```

* <a id='id2'>**[1]**</a> [Online bagging and boosting. Nikunj Oza, Stuart Russell. Artificial Intelligence and Statistics 2001.](https://proceedings.mlr.press/r3/oza01a.html)

#### \_\_init_\_(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema) | [None](https://docs.python.org/3/builtins/constants.html#None) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 0, base_learner='trees.HoeffdingTree', boosting_iterations: [int](https://docs.python.org/3/builtins/functions.html#int) = 10, use_pure_boost: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/classifier/_oza_boost.py#L39)

Incremental on-line boosting classifier of Oza and Russell.

* **Parameters:**
  * **schema** – The schema of the stream.
  * **random_seed** – The random seed passed to the MOA learner.
  * **base_learner** – The base learner to be trained. Default trees.HoeffdingTree.
  * **boosting_iterations** – The number of boosting iterations.
  * **use_pure_boost** – Boost with weights only; no poisson..

#### cli_help()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L111)

#### *classmethod* from_params(schema: [Any](https://docs.python.org/3/library/typing.html#typing.Any) = None, params: [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)] | [None](https://docs.python.org/3/builtins/constants.html#None) = None, random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1) → [Any](https://docs.python.org/3/library/typing.html#typing.Any)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L170)

Construct an instance from parameters produced by `get_params`.

#### get_params() → [dict](https://docs.python.org/3/builtins/stdtypes.html#dict)[[str](https://docs.python.org/3/builtins/stdtypes.html#str), [Any](https://docs.python.org/3/library/typing.html#typing.Any)][[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_learner_params.py#L163)

Return the hyper-parameters captured from the constructor.

#### predict(instance: [Instance](capymoa.core.Instance.md#capymoa.core.Instance)) → [int](https://docs.python.org/3/builtins/functions.html#int) | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L56)

Predict the label of an instance.

The base implementation calls [`predict_proba()`](#capymoa.classifier.OzaBoost.predict_proba) and returns the
label with the highest probability.

* **Parameters:**
  **instance** – The instance to predict the label for.
* **Returns:**
  The predicted label or `None` if the classifier is unable
  to make a prediction.

#### predict_proba(instance) → [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]] | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L117)

Return probability estimates for each label.

* **Parameters:**
  **instance** – The instance to estimate the probabilities for.
* **Returns:**
  An array of probabilities for each label or `None` if the
  classifier is unable to make a prediction.

#### train(instance)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/base/_classifier.py#L114)

Train the classifier with a labeled instance.

* **Parameters:**
  **instance** – The labeled instance to train the classifier with.

#### random_seed *: [int](https://docs.python.org/3/builtins/functions.html#int)*

The random seed for reproducibility.

When implementing a classifier ensure random number generators are seeded.

#### schema *: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)*

The schema representing the instances.