Struct rustrees::DecisionTree

pub struct DecisionTree { /* private fields */ }

Represents the decision tree model. Each node represents a split on a feature.

Implementations

impl DecisionTree

Methods for training and predicting with a decision tree. These methods are exposed to python.

pub fn train_reg( train: &Dataset, max_depth: Option<i32>, min_samples_leaf: Option<i32>, max_features: Option<i32>, random_state: Option<u64> ) -> DecisionTree

Trains a decision tree for regression. A regression tree uses the mean squared error as the split criterion.

pub fn train_clf( train: &Dataset, max_depth: Option<i32>, min_samples_leaf: Option<i32>, max_features: Option<i32>, random_state: Option<u64> ) -> DecisionTree

Trains a decision tree for classification problem. A classification tree uses the gini coefficient as the split criterion.

pub fn predict(&self, test: &Dataset) -> Vec<f32>

Predicts the target for a given dataset.

Trait Implementations

impl IntoPy<Py<PyAny>> for DecisionTree

fn into_py(self, py: Python<'_>) -> PyObject

Performs the conversion.

impl PyClass for DecisionTree

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for DecisionTree

const DOC: &'static str = "Represents the decision tree model. Each node represents a split on a feature.\u{0}"

Class doc string

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

type Layout = PyCell<DecisionTree>

Layout

type BaseType = PyAny

Base class

type ThreadChecker = ThreadCheckerStub<DecisionTree>

This handles following two situations:

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<isize>

fn weaklist_offset() -> Option<isize>

impl<'a, 'py> PyFunctionArgument<'a, 'py> for &'a DecisionTree

type Holder = Option<PyRef<'py, DecisionTree>>

fn extract(obj: &'py PyAny, holder: &'a mut Self::Holder) -> PyResult<Self>

impl<'a, 'py> PyFunctionArgument<'a, 'py> for &'a mut DecisionTree

type Holder = Option<PyRefMut<'py, DecisionTree>>

fn extract(obj: &'py PyAny, holder: &'a mut Self::Holder) -> PyResult<Self>

impl PyMethods<DecisionTree> for PyClassImplCollector<DecisionTree>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for DecisionTree

type AsRefTarget = PyCell<DecisionTree>

Utility type to make Py::as_ref work.

const NAME: &'static str = "DecisionTree"

Class name.

const MODULE: Option<&'static str> = ::core::option::Option::None

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> &PyType

Returns the safe abstraction over the type object.

fn is_type_of(object: &PyAny) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &PyAny) -> bool

Checks if object is an instance of this type.