This series of videos on machine learning algorithms (Lab a through Lab d, so far) by Courage Kamusoku are the best explanations I've seen yet. How the models actually work, their strengths and weaknesses, and what you are actually solving for when you tune the hyperparameters. https://www.youtube.com/@couragekamusoko5689/videos
#SVM #KNN #DecisionTree #RandomForest
Uhm... if I get a decision tree like the one shown in the picture, does it mean that I only need the columns shown in the tree for training and validation, right? I would only need the columns 2 and 3 (x[2], x[3]), isn't it? Or am I missing something else?
Chess decision tree (source unknown).
Unsurprising as you likely only have those planning-ahead skills in contractors (ie, not in the cheapest-vendor-the-insurance-company-has-approved)
#decisiontree
Indeed, Lognormal!
https://fooledbyrandomness.com/blog/index.php/2022/10/16/this-overhyped-zone-2-training-must-not-be-discrete/
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RT @Alan_Couzens
And a #DecisionTree applied to the same dataset.
*On a group level* (recreational->elite triathletes)..
If you want to stay to the right of the tree & have a VO2max > 60ml/kg/min. Your best "decisions":
* Train 22hrs/wk
* With >5hrs Z1
* With ~1hr/wk Z4
* With ~20min/wk Z5* https://t…
https://twitter.com/Alan_Couzens/status/1638538637158776834
