pub struct DecisionTreeRegressor<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> { /* private fields */ }
Expand description
Regression Tree
Implementations§
source§impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> DecisionTreeRegressor<TX, TY, X, Y>
impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> DecisionTreeRegressor<TX, TY, X, Y>
sourcepub fn fit(
x: &X,
y: &Y,
parameters: DecisionTreeRegressorParameters
) -> Result<DecisionTreeRegressor<TX, TY, X, Y>, Failed>
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
Trait Implementations§
source§impl<TX: Debug + Number + PartialOrd, TY: Debug + Number, X: Debug + Array2<TX>, Y: Debug + Array1<TY>> Debug for DecisionTreeRegressor<TX, TY, X, Y>
impl<TX: Debug + Number + PartialOrd, TY: Debug + Number, X: Debug + Array2<TX>, Y: Debug + Array1<TY>> Debug for DecisionTreeRegressor<TX, TY, X, Y>
source§impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq<DecisionTreeRegressor<TX, TY, X, Y>> for DecisionTreeRegressor<TX, TY, X, Y>
impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> PartialEq<DecisionTreeRegressor<TX, TY, X, Y>> for DecisionTreeRegressor<TX, TY, X, Y>
source§impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y> for DecisionTreeRegressor<TX, TY, X, Y>
impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y> for DecisionTreeRegressor<TX, TY, X, Y>
source§impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, DecisionTreeRegressorParameters> for DecisionTreeRegressor<TX, TY, X, Y>
impl<TX: Number + PartialOrd, TY: Number, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, DecisionTreeRegressorParameters> for DecisionTreeRegressor<TX, TY, X, Y>
Auto Trait Implementations§
impl<TX, TY, X, Y> RefUnwindSafe for DecisionTreeRegressor<TX, TY, X, Y>where TX: RefUnwindSafe, TY: RefUnwindSafe, X: RefUnwindSafe, Y: RefUnwindSafe,
impl<TX, TY, X, Y> Send for DecisionTreeRegressor<TX, TY, X, Y>where TX: Send, TY: Send, X: Send, Y: Send,
impl<TX, TY, X, Y> Sync for DecisionTreeRegressor<TX, TY, X, Y>where TX: Sync, TY: Sync, X: Sync, Y: Sync,
impl<TX, TY, X, Y> Unpin for DecisionTreeRegressor<TX, TY, X, Y>where TX: Unpin, TY: Unpin, X: Unpin, Y: Unpin,
impl<TX, TY, X, Y> UnwindSafe for DecisionTreeRegressor<TX, TY, X, Y>where TX: UnwindSafe, TY: UnwindSafe, X: UnwindSafe, Y: UnwindSafe,
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more