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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 — 'base.typing.ClfTarget'

Returns

Classifier: self

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.