# Splitter

Base class for the tree splitters.

Each Attribute Observer (AO) or Splitter monitors one input feature and finds the best split point for this attribute. AOs can also perform other tasks related to the monitored feature, such as estimating its probability density function (classification case).

This class should not be instantiated, as none of its methods are implemented.

## Attributes

- **is\_numeric**

  Determine whether or not the splitter works with numerical features.
- **is\_target\_class**

  Check on which kind of learning task the splitter is designed to work. If `True`, the splitter works with classification trees, otherwise it is designed for regression trees.

## Methods

> [!NOTE]
>
> **best\_evaluated\_split\_suggestion**
>
> Get the best split suggestion given a criterion and the target's statistics.
>
> **Parameters**
>
> - **criterion** — `SplitCriterion`
> - **pre\_split\_dist** — `list | dict`
> - **att\_idx** — `base.typing.FeatureName`
> - **binary\_only** — `bool`
>
> **Returns**
>
> `BranchFactory`: Suggestion of the best attribute split.

> [!NOTE]
>
> **cond\_proba**
>
> Get the probability for an attribute value given a class.
>
> **Parameters**
>
> - **att\_val**
> - **target\_val** — `base.typing.ClfTarget`
>
> **Returns**
>
> `float`: Probability for an attribute value given a class.

> [!NOTE]
>
> **update**
>
> Update statistics of this observer given an attribute value, its target value and the weight of the instance observed.
>
> **Parameters**
>
> - **att\_val**
> - **target\_val** — `base.typing.Target`
> - **w** — `float`