Trait linfa::metrics::SingleTargetRegression[−][src]

pub trait SingleTargetRegression<F: Float, T: AsTargets<Elem = F>>: AsTargets<Elem = F> {
    fn max_error(&self, compare_to: &T) -> Result<F> { ... }

    fn mean_absolute_error(&self, compare_to: &T) -> Result<F> { ... }

    fn mean_squared_error(&self, compare_to: &T) -> Result<F> { ... }

    fn mean_squared_log_error(&self, compare_to: &T) -> Result<F> { ... }

    fn median_absolute_error(&self, compare_to: &T) -> Result<F> { ... }

    fn r2(&self, compare_to: &T) -> Result<F> { ... }

    fn explained_variance(&self, compare_to: &T) -> Result<F> { ... }
}

Regression metrices trait for single targets.

It is possible to compute the listed mectrics between:

  • One-dimensional array - One-dimensional array
  • One-dimensional array - bi-dimensional array
  • One-dimensional array - dataset

In the last two cases, if the second item does not represent a single target, the result will be an error.

To compare bi-dimensional arrays use MultiTargetRegression

Provided methods

fn max_error(&self, compare_to: &T) -> Result<F>[src]

Maximal error between two continuous variables

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

Mean error between two continuous variables

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

Mean squared error between two continuous variables

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

Mean squared log error between two continuous variables

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

Median absolute error between two continuous variables

fn r2(&self, compare_to: &T) -> Result<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, compare_to: &T) -> Result<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>> SingleTargetRegression<F, T> for ArrayBase<D, Ix1>[src]

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Implementors

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