Struct smartcore::tree::decision_tree_regressor::DecisionTreeRegressor

pub struct DecisionTreeRegressor<T: RealNumber> { /* fields omitted */ }

Regression Tree

Implementations

impl<T: RealNumber> DecisionTreeRegressor<T>

pub fn fit<M: Matrix<T>>(
    x: &M,
    y: &M::RowVector,
    parameters: DecisionTreeRegressorParameters
) -> Result<DecisionTreeRegressor<T>, Failed>

Build a decision tree regressor from the training data.

• x - NxM matrix with N observations and M features in each observation.
• y - the target values

pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed>

Predict regression value for x.

• x - KxM data where K is number of observations and M is number of features.

Trait Implementations

impl<T: Debug + RealNumber> Debug for DecisionTreeRegressor<T>

pub fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de, T: RealNumber> Deserialize<'de> for DecisionTreeRegressor<T> where
    T: Deserialize<'de>, 

pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl<T: RealNumber> PartialEq<DecisionTreeRegressor<T>> for DecisionTreeRegressor<T>

pub fn eq(&self, other: &Self) -> bool

This method tests for self and other values to be equal, and is used by ==.

#[must_use]pub fn ne(&self, other: &Rhs) -> bool

This method tests for !=.

impl<T: RealNumber, M: Matrix<T>> Predictor<M, <M as BaseMatrix<T>>::RowVector> for DecisionTreeRegressor<T>

pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed>

Estimate target values from new data.

impl<T: RealNumber> Serialize for DecisionTreeRegressor<T> where
    T: Serialize, 

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, DecisionTreeRegressorParameters> for DecisionTreeRegressor<T>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: DecisionTreeRegressorParameters
) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model's parameters.