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Numeric attribute observer for classification tasks that is based on Gaussian estimators.

The distribution of each class is approximated using a Gaussian distribution. Hence, the probability density function can be easily calculated.


  • n_splits (int) – defaults to 10

    The number of partitions to consider when querying for split candidates.


  • is_numeric

    Determine whether or not the splitter works with numerical features.

  • is_target_class

    Check on which kind of learning task the splitter is designed to work. If True, the splitter works with classification trees, otherwise it is designed for regression trees.



Get the best split suggestion given a criterion and the target's statistics.


  • criterion (river.tree.split_criterion.base.SplitCriterion)
  • pre_split_dist (Union[List, Dict])
  • att_idx (Hashable)
  • binary_only (bool)


BranchFactory: Suggestion of the best attribute split.


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.


Get the probability for an attribute value given a class.


  • att_val
  • target_val (Union[bool, str, int])


float: Probability for an attribute value given a class.


Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.


  • att_val
  • target_val (Union[bool, str, int, numbers.Number])
  • sample_weight (float)