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NoChangeClassifier

Dummy classifier which returns the last class seen.

The predict_one method will output the last class seen whilst predict_proba_one will return 1 for the last class seen and 0 for the others.

Attributes

  • last_class

    The last class seen.

  • classes

    The set of classes seen.

Examples

Taken from example 2.1 from this page.

import pprint
from river import dummy

sentences = [
    ('glad happy glad', '+'),
    ('glad glad joyful', '+'),
    ('glad pleasant', '+'),
    ('miserable sad glad', '−')
]

model = dummy.NoChangeClassifier()

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

new_sentence = 'glad sad miserable pleasant glad'
model.predict_one(new_sentence)

'−'


pprint.pprint(model.predict_proba_one(new_sentence))

{'+': 0, '−': 1}

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'

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.