# `ClassificationWindowedEvaluator`

### *class* capymoa.evaluation.ClassificationWindowedEvaluator[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L698)

Bases: [`ClassificationEvaluator`](capymoa.evaluation.ClassificationEvaluator.md#capymoa.evaluation.ClassificationEvaluator)

Uses the ClassificationEvaluator to perform a windowed evaluation.

IMPORTANT: The results for the last window are not always available through ``metrics()``, if the window_size does
not perfectly divide the stream, the metrics corresponding to the last remaining instances in the last window can
be obtained by invoking ``metrics()``

#### \_\_init_\_(schema=None, window_size=1000)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L707)

#### accuracy()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L733)

#### f1_score()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L745)

#### get_instances_seen()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L154)

#### kappa()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L736)

#### kappa_m()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L742)

#### kappa_t()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L739)

#### metrics()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L231)

#### metrics_dict()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L237)

#### metrics_header()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L223)

#### metrics_per_window()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L243)

#### precision()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L748)

#### recall()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L751)

#### roc_auc()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/evaluation/evaluation.py#L754)

#### update(y_target_index: [int](https://docs.python.org/3/builtins/functions.html#int), y_pred_index: [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/evaluation/evaluation.py#L157)

Update the evaluator with the ground-truth and the prediction.

* **Parameters:**
  * **y_target_index** – The ground-truth class index. This is NOT
    the actual class value, but the index of the class value in the
    schema.
  * **y_pred_index** – The predicted class index. If the classifier
    abstains from making a prediction, this value can be None.
* **Raises:**
  [**TypeError**](https://docs.python.org/3/builtins/exceptions.html#TypeError) – If the values are not valid indexes in the schema.