# PriorClassifier¶

Dummy classifier which uses the prior distribution.

The predict_one method will output the most common class whilst predict_proba_one will return the normalized class counts.

## Attributes¶

• counts (collections.Counter)

Class counts.

• n (int)

Total number of seen instances.

## Examples¶

Taken from example 2.1 from this page

>>> from river import dummy

>>> sentences = [
...     ('glad pleasant', '+'),
... ]

>>> model = dummy.PriorClassifier()

>>> for sentence, label in sentences:
...     model = model.learn_one(sentence, label)

>>> model.predict_one(new_sentence)
'+'
>>> model.predict_proba_one(new_sentence)
{'+': 0.75, '−': 0.25}


## Methods¶

clone

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.

learn_one

Update the model with a set of features x and a label y.

Parameters

• x (dict)
• y (Union[bool, str, int])

Returns

Classifier: self

predict_one

Predict the label of a set of features x.

Parameters

• x (dict)

Returns

typing.Union[bool, str, int]: The predicted label.

predict_proba_one

Predict the probability of each label for a dictionary of features x.

Parameters

• x (dict)

Returns

typing.Dict[typing.Union[bool, str, int], float]: A dictionary that associates a probability which each label.