Type Alias linfa_logistic::ValidLogisticRegression
source · pub type ValidLogisticRegression<F> = LogisticRegressionValidParams<F, Ix1>;Expand description
Validated version of LogisticRegression
Aliased Type§
struct ValidLogisticRegression<F> { /* private fields */ }Trait Implementations§
source§impl<'a, C: 'a + Ord + Clone, F: Float, D: Data<Elem = F>, T: AsSingleTargets<Elem = C>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, Error> for ValidLogisticRegression<F>
impl<'a, C: 'a + Ord + Clone, F: Float, D: Data<Elem = F>, T: AsSingleTargets<Elem = C>> Fit<ArrayBase<D, Dim<[usize; 2]>>, T, Error> for ValidLogisticRegression<F>
source§fn fit(
&self,
dataset: &DatasetBase<ArrayBase<D, Ix2>, T>
) -> Result<Self::Object>
fn fit( &self, dataset: &DatasetBase<ArrayBase<D, Ix2>, T> ) -> Result<Self::Object>
Given a 2-dimensional feature matrix array x with shape
(n_samples, n_features) and an array of target classes to predict,
create a FittedLinearRegression object which allows making
predictions.
The array of target classes y must have exactly two discrete values, (e.g. 0 and 1, “cat”
and “dog”, …), which represent the two different classes the model is supposed to
predict.
The array y must also have exactly n_samples items, i.e.
exactly as many items as there are rows in the feature matrix x.
This method returns an error if any of the preconditions are violated,
i.e. any values are Inf or NaN, y doesn’t have as many items as
x has rows, or if other parameters (gradient_tolerance, alpha) have
been set to inalid values.