# HoeffdingTreeClassifier

Hoeffding Tree or Very Fast Decision Tree classifier.

## Parameters

- **grace\_period** *(mutable)*

  *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.
- **split\_criterion**

  *Type* → `str`

  *Default* → `info_gain`

  Split criterion to use. - 'gini' - Gini - 'info\_gain' - Information Gain - 'hellinger' - Helinger Distance
- **delta** *(mutable)*

  *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** *(mutable)*

  *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

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

metric = metrics.Accuracy()

evaluate.progressive_val_score(dataset, model, metric)
```

```text
Accuracy: 84.58%
```

## Methods

> [!NOTE]
>
> **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

> [!NOTE]
>
> **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.

> [!NOTE]
>
> **learn\_one**
>
> Train the model on instance x and corresponding target y.
>
> **Parameters**
>
> - **x**
> - **y**
> - **w** — defaults to `1.0`

> [!NOTE]
>
> **predict\_one**
>
> Predict the label of a set of features `x`.
>
> **Parameters**
>
> - **x**
> - **kwargs**
>
> **Returns**
>
> The predicted label.

> [!NOTE]
>
> **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.

> [!NOTE]
>
> **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 [^/dev/api/tree/HoeffdingTreeClassifier/#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 [^/dev/api/tree/HoeffdingTreeClassifier/#2].

[^/dev/api/tree/HoeffdingTreeClassifier/#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.

[^/dev/api/tree/HoeffdingTreeClassifier/#2]: Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer. MOA: Massive Online Analysis; Journal of Machine Learning Research 11: 1601-1604, 2010.