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.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 — 'base.typing.ClfTarget'
predict_one
Predict the label of a set of features x.
Parameters
- x — 'dict'
- kwargs
Returns
base.typing.ClfTarget | None: The predicted label.
predict_proba_one
Predict the probability of each label for a dictionary of features x.
Parameters
- x — 'dict'
Returns
dict[base.typing.ClfTarget, float]: A dictionary that associates a probability which each label.