Preprocessing quantileoutput

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QuantileOutput

Which distribution QuantileTransformer maps its ranks onto.

public enum QuantileOutput

Values — Uniform maps onto the unit interval; scikit-learn's 'uniform', and the default. Normal maps onto the standard normal, through its quantile function; 'normal'.

Example — the two ends of a column, on the normal output.

using Lodestar.Preprocessing;

double[] skew = [1.0, 1.0, 2.0, 3.0, 5.0, 8.0, 13.0, 21.0, 34.0, 100.0];

QuantileTransformer normal = QuantileTransformer.Fit(
    skew, 1, new QuantileTransformerOptions { Output = QuantileOutput.Normal });

double[] ends = normal.Transform([1.0, 100.0]);

double low = ends[0];    // => -5.199337582605575
double high = ends[1];   // => 5.19933758270342

Remarks — Normal is Uniform composed with the normal quantile function, so the two carry the same information and differ in what a downstream model sees: a uniform column has no tails and a normal one does, which matters to anything that assumes normality — a linear model's residuals, a Gaussian naive Bayes, a distance in a space where the extremes should count for more.

The ends are clipped rather than infinite. The normal quantile of 0 is -∞, which no model can use, so both ends stop at the quantile of 1e-7 one ulp in — about ±5.199. That is the reference's own threshold, and the asymmetry in the last digits above is the quantile function being evaluated at two arguments that are not exact mirrors.

Applies to — net10.0, netstandard2.0.

See also — QuantileTransformerOptions, QuantileTransformer.Transform.