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HoeffdingTreeClassifier

Hoeffding Tree or Very Fast Decision Tree classifier.

Parameters

  • grace_period (int) – defaults to 200

    Number of instances a leaf should observe between split attempts.

  • max_depth (int) – defaults to None

    The maximum depth a tree can reach. If None, the tree will grow indefinitely.

  • split_criterion (str) – defaults to info_gain

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

  • delta (float) – defaults to 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 (float) – defaults to 0.05

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

  • leaf_prediction (str) – defaults to nba

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

  • nb_threshold (int) – defaults to 0

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

  • nominal_attributes (list) – defaults to None

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

  • splitter (river.tree.splitter.base.Splitter) – defaults to 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 (bool) – defaults to False

    If True, only allow binary splits.

  • max_size (float) – defaults to 100.0

    The max size of the tree, in Megabytes (MB).

  • memory_estimate_period (int) – defaults to 1000000

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

  • stop_mem_management (bool) – defaults to False

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

  • remove_poor_attrs (bool) – defaults to False

    If True, disable poor attributes to reduce memory usage.

  • merit_preprune (bool) – defaults to 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 MB).

  • 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)
>>> # Take 1000 instances from the infinite data generator
>>> dataset = iter(gen.take(1000))

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

>>> metric = metrics.Accuracy()

>>> evaluate.progressive_val_score(dataset, model, metric)
Accuracy: 83.78%

Methods

debug_one

Print an explanation of how x is predicted.

Parameters

  • x (dict)

Returns

typing.Optional[str]: 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) – defaults to None
    The maximum depth a tree can reach. If None, the tree will grow indefinitely.
learn_one

Train the model on instance x and corresponding target y.

Parameters

  • x
  • y
  • sample_weight – defaults to 1.0

Returns

self

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.

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

A Hoeffding Tree 1 is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not change over time. Hoeffding trees exploit the fact that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound, which quantifies the number of observations (in our case, examples) needed to estimate some statistics within a prescribed precision (in our case, the goodness of an attribute).

A theoretically appealing feature of Hoeffding Trees not shared by other incremental decision tree learners is that it has sound guarantees of performance. Using the Hoeffding bound one can show that its output is asymptotically nearly identical to that of a non-incremental learner using infinitely many examples. Implementation based on MOA 2.

References

  1. G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In KDD’01, pages 97–106, San Francisco, CA, 2001. ACM Press. ↩

  2. Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010. ↩