Trait linfa::metrics::MultiTargetRegression

source · 
pub trait MultiTargetRegression<F: Float, T: AsMultiTargets<Elem = F>>: AsMultiTargets<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 mean_absolute_percentage_error(&self, other: &T) -> Result<Array1<F>> { ... }
    fn r2(&self, other: &T) -> Result<Array1<F>> { ... }
    fn explained_variance(&self, other: &T) -> Result<Array1<F>> { ... }
}
Expand description

Regression metrices trait for multiple targets.

It is possible to compute the listed mectrics between two 2D arrays. To compare single-dimensional arrays use SingleTargetRegression.

Provided Methods§

Maximal error between two continuous variables

Mean error between two continuous variables

Mean squared error between two continuous variables

Mean squared log error between two continuous variables

Median absolute error between two continuous variables

Mean absolute percentage error between two continuous variables MAPE = 1/N * SUM(abs((y_hat - y) / y))

R squared coefficient, is the proportion of the variance in the dependent variable that is predictable from the independent variable

Same as R-Squared but with biased variance

Implementations on Foreign Types§

Implementors§