# `OnlineSmoothBoost`

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

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

Online Smooth Boost.

Online Smooth Boost <sup>[1](#id2)</sup> is a ensemble classifier. Incremental on-line boosting
with Theoretical Justifications of Shang-Tse Chen.

```pycon
>>> from capymoa.classifier import OnlineSmoothBoost
>>> from capymoa.datasets import ElectricityTiny
>>> from capymoa.evaluation import prequential_evaluation
>>>
>>> stream = ElectricityTiny()
>>> classifier = OnlineSmoothBoost(stream.get_schema())
>>> results = prequential_evaluation(stream, classifier, max_instances=1000)
>>> print(f"{results['cumulative'].accuracy():.1f}")
87.8
```

* <a id='id2'>**[1]**</a> [An Online Boosting Algorithm with Theoretical Justifications. Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu. ICML, 2012.](https://icml.cc/2012/papers/538.pdf)

#### \_\_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) = 100, gamma=0.1)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/classifier/_online_smooth_boost.py#L33)

OnlineSmoothBoost Classifier

* **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 (ensemble size).
  * **gamma** – The value of the gamma parameter.

#### 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.OnlineSmoothBoost.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.