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Kappa-T score.

The Kappa-T measures the temporal correlation between samples. It is defined as

\[ \kappa_{t} = (p_o - p_e) / (1 - p_e) \]

where \(p_o\) is the empirical probability of agreement on the label assigned to any sample (prequential accuracy), and \(p_e\) is the prequential accuracy of the no-change classifier that predicts only using the last class seen by the classifier.


  • cm (river.metrics.confusion.ConfusionMatrix) – defaults to None

    This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.


  • bigger_is_better

    Indicate if a high value is better than a low one or not.

  • requires_labels

    Indicates if labels are required, rather than probabilities.

  • sample_correction

  • works_with_weights

    Indicate whether the model takes into consideration the effect of sample weights


>>> from river import metrics

>>> y_true = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird']
>>> y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat']

>>> metric = metrics.KappaT()

>>> for yt, yp in zip(y_true, y_pred):
...     metric = metric.update(yt, yp)

>>> metric
KappaT: 0.6



Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.


Return the current value of the metric.


Revert the metric.


  • y_true
  • y_pred
  • sample_weight – defaults to 1.0
  • correction – defaults to None

Update the metric.


  • y_true
  • y_pred
  • sample_weight – defaults to 1.0

Indicates whether or not a metric can work with a given model.


  • model (river.base.estimator.Estimator)


  1. A. Bifet et al. (2013). "Pitfalls in benchmarking data stream classification and how to avoid them." Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), Springer LNAI 8188, p. 465-479.