[−][src]Enum opencv::ml::Boost_Types
Boosting type. Gentle AdaBoost and Real AdaBoost are often the preferable choices.
Variants
Discrete AdaBoost.
Real AdaBoost. It is a technique that utilizes confidence-rated predictions and works well with categorical data.
LogitBoost. It can produce good regression fits.
Gentle AdaBoost. It puts less weight on outlier data points and for that reason is often good with regression data.
Trait Implementations
impl Clone for Boost_Types
[src]
pub fn clone(&self) -> Boost_Types
[src]
pub fn clone_from(&mut self, source: &Self)
1.0.0[src]
impl Copy for Boost_Types
[src]
impl Debug for Boost_Types
[src]
impl PartialEq<Boost_Types> for Boost_Types
[src]
pub fn eq(&self, other: &Boost_Types) -> bool
[src]
#[must_use]pub fn ne(&self, other: &Rhs) -> bool
1.0.0[src]
impl StructuralPartialEq for Boost_Types
[src]
Auto Trait Implementations
impl RefUnwindSafe for Boost_Types
[src]
impl Send for Boost_Types
[src]
impl Sync for Boost_Types
[src]
impl Unpin for Boost_Types
[src]
impl UnwindSafe for Boost_Types
[src]
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
[src]
T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
[src]
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
[src]
T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
[src]
impl<T> From<T> for T
[src]
impl<T, U> Into<U> for T where
U: From<T>,
[src]
U: From<T>,
impl<T> ToOwned for T where
T: Clone,
[src]
T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
[src]
pub fn clone_into(&self, target: &mut T)
[src]
impl<T, U> TryFrom<U> for T where
U: Into<T>,
[src]
U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
[src]
impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
[src]
U: TryFrom<T>,