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>>[src]

Maximal error between two continuous variables

fn mean_absolute_error(&self, other: &T) -> Result<Array1<F>>[src]

Mean error between two continuous variables

fn mean_squared_error(&self, other: &T) -> Result<Array1<F>>[src]

Mean squared error between two continuous variables

fn mean_squared_log_error(&self, other: &T) -> Result<Array1<F>>[src]

Mean squared log error between two continuous variables

fn median_absolute_error(&self, other: &T) -> Result<Array1<F>>[src]

Median absolute error between two continuous variables

fn r2(&self, other: &T) -> Result<Array1<F>>[src]

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>>[src]

Same as R-Squared but with biased variance

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Implementations on Foreign Types

impl<F: Float, D: Data<Elem = F>, T: AsTargets<Elem = F>> MultiTargetRegression<F, T> for ArrayBase<D, Ix2>[src]

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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>[src]

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