lda¶

Linear Discriminant Analysis projection.

Use method="lda" to find axes that maximize separation between classes in labels while accounting for within-class variance.

When to choose this method¶

Choose LDA when you want supervised projections that remain interpretable and naturally support multiclass settings.

Maximizes between-class separation relative to within-class spread.
Often works well when classes are reasonably Gaussian in feature space.
Provides compact discriminant axes that can be easier to interpret than generic classifier coefficients.

LDA can degrade when class covariance assumptions are badly violated or when sample size is small relative to dimensionality.

ds = da.ephys.reduce(
    method="lda",
    labels="choice",
)