ExtremelyFastDecisionTreeClassifier¶

Extremely Fast Decision Tree (EFDT) classifier.

Also referred to as the Hoeffding AnyTime Tree (HATT) classifier. In practice, despite the name, EFDTs are typically slower than a vanilla Hoeffding Tree to process data. The speed differences come from the mechanism of split re-evaluation present in EFDT. Nonetheless, EFDT has theoretical properties that ensure it converges faster than the vanilla Hoeffding Tree to the structure that would be created by a batch decision tree model (such as Classification and Regression Trees - CART). Keep in mind that such propositions hold when processing a stationary data stream. When dealing with non-stationary data, EFDT is somewhat robust to concept drifts as it continually revisits and updates its internal decision tree structure. Still, in such cases, the Hoeffind Adaptive Tree might be a better option, as it was specifically designed to handle non-stationarity.

Parameters¶

grace_period

Type → int

Default → 200

Number of instances a leaf should observe between split attempts.
max_depth

Type → int | None

Default → None

The maximum depth a tree can reach. If None, the tree will grow until the system recursion limit.
min_samples_reevaluate

Type → int

Default → 20

Number of instances a node should observe before reevaluating the best split.
split_criterion

Type → str

Default → info_gain

Split criterion to use.
- 'gini' - Gini
- 'info_gain' - Information Gain
- 'hellinger' - Helinger Distance
delta

Type → float

Default → 1e-07

Significance level to calculate the Hoeffding bound. The significance level is given by 1 - delta. Values closer to zero imply longer split decision delays.
tau

Type → float

Default → 0.05

Threshold below which a split will be forced to break ties.
leaf_prediction

Type → str

Default → nba

Prediction mechanism used at leafs.
- 'mc' - Majority Class
- 'nb' - Naive Bayes
- 'nba' - Naive Bayes Adaptive
nb_threshold

Type → int

Default → 0

Number of instances a leaf should observe before allowing Naive Bayes.
nominal_attributes

Type → list | None

Default → None

List of Nominal attributes identifiers. If empty, then assume that all numeric attributes should be treated as continuous.
splitter

Type → Splitter | None

Default → None

The Splitter or Attribute Observer (AO) used to monitor the class statistics of numeric features and perform splits. Splitters are available in the tree.splitter module. Different splitters are available for classification and regression tasks. Classification and regression splitters can be distinguished by their property is_target_class. This is an advanced option. Special care must be taken when choosing different splitters. By default, tree.splitter.GaussianSplitter is used if splitter is None.
binary_split

Type → bool

Default → False

If True, only allow binary splits.
min_branch_fraction

Type → float

Default → 0.01

The minimum percentage of observed data required for branches resulting from split candidates. To validate a split candidate, at least two resulting branches must have a percentage of samples greater than min_branch_fraction. This criterion prevents unnecessary splits when the majority of instances are concentrated in a single branch.
max_share_to_split

Type → float

Default → 0.99

Only perform a split in a leaf if the proportion of elements in the majority class is smaller than this parameter value. This parameter avoids performing splits when most of the data belongs to a single class.
max_size

Type → float

Default → 100.0

The max size of the tree, in mebibytes (MiB).
memory_estimate_period

Type → int

Default → 1000000

Interval (number of processed instances) between memory consumption checks.
stop_mem_management

Type → bool

Default → False

If True, stop growing as soon as memory limit is hit.
remove_poor_attrs

Type → bool

Default → False

If True, disable poor attributes to reduce memory usage.
merit_preprune

Type → bool

Default → True

If True, enable merit-based tree pre-pruning.

Attributes¶

height
leaf_prediction

Return the prediction strategy used by the tree at its leaves.
max_size

Max allowed size tree can reach (in MiB).
n_active_leaves
n_branches
n_inactive_leaves
n_leaves
n_nodes
split_criterion

Return a string with the name of the split criterion being used by the tree.
summary

Collect metrics corresponding to the current status of the tree in a string buffer.

Examples¶

from river.datasets import synth
from river import evaluate
from river import metrics
from river import tree

gen = synth.Agrawal(classification_function=0, seed=42)
dataset = iter(gen.take(1000))

model = tree.ExtremelyFastDecisionTreeClassifier(
    grace_period=100,
    delta=1e-5,
    nominal_attributes=['elevel', 'car', 'zipcode'],
    min_samples_reevaluate=100
)

metric = metrics.Accuracy()

evaluate.progressive_val_score(dataset, model, metric)

Accuracy: 87.29%

Methods¶

debug_one

Print an explanation of how x is predicted.

Parameters

x — 'dict'

Returns

str | None: A representation of the path followed by the tree to predict x; None if

draw

Draw the tree using the graphviz library.

Since the tree is drawn without passing incoming samples, classification trees will show the majority class in their leaves, whereas regression trees will use the target mean.

Parameters

max_depth — 'int | None' — defaults to None
The maximum depth a tree can reach. If None, the tree will grow until the system recursion limit.

learn_one

Incrementally train the model

Parameters

x
y
w — defaults to 1.0

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.

to_dataframe

Return a representation of the current tree structure organized in a pandas.DataFrame object.

In case the tree is empty or it only contains a single node (a leaf), None is returned.

Returns

df

Notes¶

The Extremely Fast Decision Tree (EFDT) ¹ constructs a tree incrementally. The EFDT seeks to select and deploy a split as soon as it is confident the split is useful, and then revisits that decision, replacing the split if it subsequently becomes evident that a better split is available. The EFDT learns rapidly from a stationary distribution and eventually it learns the asymptotic batch tree if the distribution from which the data are drawn is stationary.

C. Manapragada, G. Webb, and M. Salehi. Extremely Fast Decision Tree. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). ACM, New York, NY, USA, 1953-1962. DOI: https://doi.org/10.1145/3219819.3220005 ↩