AMFClassifier¶
Aggregated Mondrian Forest classifier for online learning.
This implementation is truly online^{1}, in the sense that a single pass is performed, and that predictions can be produced anytime.
Each node in a tree predicts according to the distribution of the labels it contains. This distribution is regularized using a "Jeffreys" prior with parameter dirichlet
. For each class with count
labels in the node and n_samples
samples in it, the prediction of a node is given by
\(\frac{count + dirichlet}{n_{samples} + dirichlet \times n_{classes}}\).
The prediction for a sample is computed as the aggregated predictions of all the subtrees along the path leading to the leaf node containing the sample. The aggregation weights are exponential weights with learning rate step
and logloss when use_aggregation
is True
.
This computation is performed exactly thanks to a context tree weighting algorithm. More details can be found in the paper cited in the references below.
The final predictions are the average class probabilities predicted by each of the n_estimators
trees in the forest.
Parameters¶

n_estimators
Type → int
Default →
10
The number of trees in the forest.

step
Type → float
Default →
1.0
Stepsize for the aggregation weights. Default is 1 for classification with the logloss, which is usually the best choice.

use_aggregation
Type → bool
Default →
True
Controls if aggregation is used in the trees. It is highly recommended to leave it as
True
. 
dirichlet
Type → float
Default →
0.5
Regularization level of the class frequencies used for predictions in each node. A rule of thumb is to set this to
1 / n_classes
, wheren_classes
is the expected number of classes which might appear. Default isdirichlet = 0.5
, which works well for binary classification problems. 
split_pure
Type → bool
Default →
False
Controls if nodes that contains only sample of the same class should be split ("pure" nodes). Default is
False
, namely pure nodes are not split, butTrue
can be sometimes better. 
seed
Type → int  None
Default →
None
Random seed for reproducibility.
Attributes¶
 models
Examples¶
from river import datasets
from river import evaluate
from river import forest
from river import metrics
dataset = datasets.Bananas().take(500)
model = forest.AMFClassifier(
n_estimators=10,
use_aggregation=True,
dirichlet=0.5,
seed=1
)
metric = metrics.Accuracy()
evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 85.37%
Methods¶
learn_one
Update the model with a set of features x
and a label y
.
Parameters
 x
 y
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
Returns
A dictionary that associates a probability which each label.
Notes¶
Only log_loss used for the computation of the aggregation weights is supported for now, namely the logloss for multiclass classification.

Mourtada, J., Gaïffas, S., & Scornet, E. (2021). AMF: Aggregated Mondrian forests for online learning. Journal of the Royal Statistical Society Series B: Statistical Methodology, 83(3), 505533. ↩