AdaBoostClassifier¶
Boosting for classification.
For each incoming observation, each model's learn_one method is called k times where k is sampled from a Poisson distribution of parameter lambda. The lambda parameter is updated when the weaks learners fit successively the same observation.
Parameters¶
-
model
Type → base.Classifier
The classifier to boost.
-
n_models
Default →
10The number of models in the ensemble.
-
seed
Type → int | None
Default →
NoneRandom number generator seed for reproducibility.
Attributes¶
- models
Examples¶
In the following example three tree classifiers are boosted together. The performance is slightly better than when using a single tree.
from river import datasets
from river import ensemble
from river import evaluate
from river import metrics
from river import tree
dataset = datasets.Phishing()
metric = metrics.LogLoss()
model = ensemble.AdaBoostClassifier(
model=(
tree.HoeffdingTreeClassifier(
split_criterion='gini',
delta=1e-5,
grace_period=2000
)
),
n_models=5,
seed=42
)
evaluate.progressive_val_score(dataset, model, metric)
LogLoss: 0.370805
print(model)
AdaBoostClassifier(HoeffdingTreeClassifier)
Methods¶
learn_one
Update the model with a set of features x and a label y.
Parameters
- x
- y
- kwargs
Returns
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
- kwargs
Returns
A dictionary that associates a probability which each label.