Trait linfa::metrics::MultiTargetRegression [−][src]
pub trait MultiTargetRegression<F: Float, T: AsTargets<Elem = F>>: AsTargets<Elem = F> { fn max_error(&self, other: &T) -> Result<Array1<F>> { ... } fn mean_absolute_error(&self, other: &T) -> Result<Array1<F>> { ... } fn mean_squared_error(&self, other: &T) -> Result<Array1<F>> { ... } fn mean_squared_log_error(&self, other: &T) -> Result<Array1<F>> { ... } fn median_absolute_error(&self, other: &T) -> Result<Array1<F>> { ... } fn r2(&self, other: &T) -> Result<Array1<F>> { ... } fn explained_variance(&self, other: &T) -> Result<Array1<F>> { ... } }
Regression metrices trait for multiple targets.
It is possible to compute the listed mectrics between:
- bi-dimensional array - bi-dimensional array
- bi-dimensional array - dataset
- dataset - dataset
- dataset - one-dimensional array
- dataset - bi-dimensional array
The shape of the compared targets must match.
To compare single-dimensional arrays use SingleTargetRegression
Provided methods
fn max_error(&self, other: &T) -> Result<Array1<F>>
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Maximal error between two continuous variables
fn mean_absolute_error(&self, other: &T) -> Result<Array1<F>>
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Mean error between two continuous variables
fn mean_squared_error(&self, other: &T) -> Result<Array1<F>>
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Mean squared error between two continuous variables
fn mean_squared_log_error(&self, other: &T) -> Result<Array1<F>>
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Mean squared log error between two continuous variables
fn median_absolute_error(&self, other: &T) -> Result<Array1<F>>
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Median absolute error between two continuous variables
fn r2(&self, other: &T) -> Result<Array1<F>>
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R squared coefficient, is the proportion of the variance in the dependent variable that is predictable from the independent variable
fn explained_variance(&self, other: &T) -> Result<Array1<F>>
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Same as R-Squared but with biased variance
Implementations on Foreign Types
impl<F: Float, D: Data<Elem = F>, T: AsTargets<Elem = F>> MultiTargetRegression<F, T> for ArrayBase<D, Ix2>
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impl<F: Float, D: Data<Elem = F>, T: AsTargets<Elem = F>> MultiTargetRegression<F, T> for ArrayBase<D, Ix2>
[src]Implementors
impl<F: Float, T: AsTargets<Elem = F>, T2: AsTargets<Elem = F>, D: Data<Elem = F>> MultiTargetRegression<F, T2> for DatasetBase<ArrayBase<D, Ix2>, T>
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impl<F: Float, T: AsTargets<Elem = F>, T2: AsTargets<Elem = F>, D: Data<Elem = F>> MultiTargetRegression<F, T2> for DatasetBase<ArrayBase<D, Ix2>, T>
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