From e854c9ea7d765238ccd95b274883fbd17c08e6b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nu=C3=B1o=20Sempere?= Date: Sat, 12 Oct 2019 19:38:34 +0200 Subject: [PATCH] Update readme.md --- maths-prog/MachineLearningDemystified/readme.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/maths-prog/MachineLearningDemystified/readme.md b/maths-prog/MachineLearningDemystified/readme.md index 16051e9..bb381dc 100644 --- a/maths-prog/MachineLearningDemystified/readme.md +++ b/maths-prog/MachineLearningDemystified/readme.md @@ -27,7 +27,7 @@ It came as a surprise to me that understanding and implementing the algorithm we - Those who have 4+ children get charged less by insurance, and smoke less. ![](children-charge-smoking.png) -- The disgreggation by age seems interesting, because there are three prongs, roughly: 1) normal people who don't smoke, 2) those who get charged more: made out of those who don't smoke, and 3) those who get charged a lot, which only comprises smokers. The Gaussian Mixture & K-Means algorithms do better than most others at discriminating between these threee groups, and made me realize the difference. +- The disgreggation by age seems interesting, because there are three prongs, roughly: 1) normal people who don't smoke, 2) those who get charged more: made out of both smokers and nonsmokers, and 3) those who get charged a lot, which only comprises smokers. The Gaussian Mixture & K-Means algorithms do better than most others at discriminating between these threee groups, and made me realize the difference. ![](GaussianMixture-age.png) ![](GaussianMixture-smoker_numeric.png)