[HN Gopher] How to Visualize Decision Trees
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       How to Visualize Decision Trees
        
       Author : LewisVerstappen
       Score  : 219 points
       Date   : 2021-09-28 09:20 UTC (13 hours ago)
        
 (HTM) web link (explained.ai)
 (TXT) w3m dump (explained.ai)
        
       | julbook wrote:
       | Great explanation; I understand different visual tree
       | orientations. I agree with the lesson learned section; it's not
       | about programming alone but also about determining the
       | ecosystem's capabilities.
        
       | mistrial9 wrote:
       | kudos for the clean HTML document
        
         | parrt wrote:
         | Thanks. It's morphed over time as we add functionality so it's
         | less clean than before.
        
       | Torwald wrote:
       | In the "Default scikit Iris visualization" example you could
       | colorize the arrows for true|false since colors are available.
       | 
       | Alternatively you could use two distinct types of arrowheads.
        
         | parrt wrote:
         | That's a good idea. thanks!
        
       | parrt wrote:
       | Also note we recently added 1D and 2D classifier decision
       | boundary plots. See
       | https://github.com/parrt/dtreeviz/blob/master/notebooks/clas...
        
       | jononor wrote:
       | Very nice work. Glad to see both classification and regression
       | treated very well, with careful attention to design to make
       | something that is easy to understand.
       | 
       | Now the question is - can we build on this (or do something
       | analogous) for tree ensembles? Random Forests, Gradient Boosted
       | Trees etc. Quite common to use that to gain predictive accuracy,
       | though interpretability/explainability tends to suffer
       | considerably.
        
         | parrt wrote:
         | Thanks! It took forever to bash my way to victory on that
         | trees. The lib also supports the shallow trees in boosting
         | machines.
        
       | niyyou wrote:
       | And his visualization of constrained optimization is astonishing
       | https://explained.ai/regularization/index.html (I struggled for a
       | long time to get the right intuition of a Lagrangian)
        
         | parrt wrote:
         | Thanks! Took me a year to discover the key nut there. L1 vs L2
         | regularization is not well described I found so I went nuts
         | trying to nail it down.
        
           | bravura wrote:
           | If you're interested, in my thesis I induced l1-regularized
           | decision trees through a boosting style approach. Adding an
           | l1 term and maximizing the gradient led to sparse tree.
        
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       (page generated 2021-09-28 23:01 UTC)