From 963074fec76b107948e3a33c0d238c6ba51bb3bb Mon Sep 17 00:00:00 2001 From: vincent d warmerdam Date: Thu, 31 Oct 2024 16:54:02 +0100 Subject: [PATCH] gogo --- docs/user-guide/preprocessing.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/user-guide/preprocessing.md b/docs/user-guide/preprocessing.md index 8880841b..5fcd13b9 100644 --- a/docs/user-guide/preprocessing.md +++ b/docs/user-guide/preprocessing.md @@ -228,6 +228,11 @@ Finally, let's show how these features might compare with an isotonic regression ![monotonic-spline-regr](../_static/preprocessing/monotonic-spline-regr.png) +While the `IsotonicRegression` gives a similar result, there are a few reasons why the monotonic spline might be preferred: + +1. The monotonic model can result in a smoother model when followed up by a linear model. The linear model can still guarantee monotonicity, but the `IsotonicRegression` might result in a spiky output. +2. When datasets get big, especially when there are many features involved, the monotonic spline might be faster to compute. This is because the `IsotonicRegression` demands a more complex solver that might not scale as well as a linear model. + ## Interval Encoders Sometimes a linear regression doesn't entirely do what you'd like. Take this pattern;