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 happy glad', '+'),
... ('glad glad joyful', '+'),
... ('glad pleasant', '+'),
... ('miserable sad glad', '−')
... ]
>>> model = dummy.PriorClassifier()
>>> for sentence, label in sentences:
... model = model.learn_one(sentence, label)
>>> new_sentence = 'glad sad miserable pleasant glad'
>>> model.predict_one(new_sentence)
'+'
>>> model.predict_proba_one(new_sentence)
{'+': 0.75, '−': 0.25}
Methods¶
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)
- kwargs
Returns
typing.Union[bool, str, int, NoneType]: 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.