StackingClassifier¶
Stacking for binary classification.
Parameters¶
-
models (List[base.Classifier])
-
meta_classifier (base.Classifier)
-
include_features – defaults to
TrueIndicates 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)
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