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StackingClassifier

Stacking for binary classification.

Parameters

  • models (List[base.Classifier])

  • meta_classifier (base.Classifier)

  • include_features – defaults to True

    Indicates whether or not the original features should be provided to the meta-model along with the predictions from each model.

Attributes

  • models

Examples

>>> from river import compose
>>> from river import datasets
>>> from river import ensemble
>>> from river import evaluate
>>> from river import linear_model as lm
>>> from river import metrics
>>> from river import preprocessing as pp

>>> dataset = datasets.Phishing()

>>> model = compose.Pipeline(
...     ('scale', pp.StandardScaler()),
...     ('stack', ensemble.StackingClassifier(
...         [
...             lm.LogisticRegression(),
...             lm.PAClassifier(mode=1, C=0.01),
...             lm.PAClassifier(mode=2, C=0.01),
...         ],
...         meta_classifier=lm.LogisticRegression()
...     ))
... )

>>> metric = metrics.F1()

>>> evaluate.progressive_val_score(dataset, model, metric)
F1: 88.14%

Methods

append

S.append(value) -- append value to the end of the sequence

Parameters

  • item
clear

S.clear() -> None -- remove all items from S

copy
count

S.count(value) -> integer -- return number of occurrences of value

Parameters

  • item
extend

S.extend(iterable) -- extend sequence by appending elements from the iterable

Parameters

  • other
index

S.index(value, [start, [stop]]) -> integer -- return first index of value. Raises ValueError if the value is not present.

Supporting start and stop arguments is optional, but recommended.

Parameters

  • item
  • args
insert

S.insert(index, value) -- insert value before index

Parameters

  • i
  • item
learn_one

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

Parameters

  • x
  • y

Returns

self

pop

S.pop([index]) -> item -- remove and return item at index (default last). Raise IndexError if list is empty or index is out of range.

Parameters

  • i – defaults to -1
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

Returns

A dictionary that associates a probability which each label.

remove

S.remove(value) -- remove first occurrence of value. Raise ValueError if the value is not present.

Parameters

  • item
reverse

S.reverse() -- reverse IN PLACE

sort

References