Struct DecisionTreeRegressor

pub struct DecisionTreeRegressor<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> { /* private fields */ }

Regression Tree

Implementations

impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> DecisionTreeRegressor<TX, TY, X, Y>

pub fn fit( x: &X, y: &Y, parameters: DecisionTreeRegressorParameters, ) -> Result<DecisionTreeRegressor<TX, TY, X, Y>, 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(&self, x: &X) -> Result<Y, Failed>

Predict regression value for x.

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

Trait Implementations

impl<TX: Debug + Number + PartialOrd, TY: Debug + Number, X: Debug + Array2<TX>, Y: Debug + Array1<TY>> Debug for DecisionTreeRegressor<TX, TY, X, Y>

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

Formats the value using the given formatter.

impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq for DecisionTreeRegressor<TX, TY, X, Y>

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

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y> for DecisionTreeRegressor<TX, TY, X, Y>

fn predict(&self, x: &X) -> Result<Y, Failed>

Estimate target values from new data.

impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, DecisionTreeRegressorParameters> for DecisionTreeRegressor<TX, TY, X, Y>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit( x: &X, y: &Y, parameters: DecisionTreeRegressorParameters, ) -> Result<Self, Failed>

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