[HN Gopher] Pybaobab - Python implementation of visualization te...
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       Pybaobab - Python implementation of visualization technique for
       decision trees
        
       Author : sebg
       Score  : 112 points
       Date   : 2021-12-02 15:16 UTC (7 hours ago)
        
 (HTM) web link (gitlab.tue.nl)
 (TXT) w3m dump (gitlab.tue.nl)
        
       | time_to_smile wrote:
       | I suspect decisions trees are still highly under utilized for
       | optimizing any human in the loop processes that require an actual
       | script that a person needs to follow.
       | 
       | It's not an uncommon problem that you're faced with needing to
       | make a series of decisions in a business environment and old-
       | school decision trees give a remarkably clear, readable output of
       | the optimal way to make these decisions in as few choices as
       | possible.
       | 
       | Any data scientists working on teams with call centers, sales
       | teams or customer support people would likely find a surprisingly
       | useful application of this mostly forgotten (other than a
       | building block for RFs) tool.
        
         | jmmcd wrote:
         | Agreed... but the decision trees we learn from data to do
         | classification are quite different from the decision trees we
         | design to formalise our processes. I think several replies here
         | have missed the distinction.
        
         | tsumnia wrote:
         | Its not that decision trees are under utilized, but rather one
         | of their disadvantages is that they can overfit to their
         | training data very easily. This is actually one of the reasons
         | Random Forests exist - just make a lot of overfitted decision
         | trees and then vote for consensus.
         | 
         | The visualization looks great, though it did suffer visualizing
         | the Random Forest. I could see using it for a single Decision
         | Tree to convey the data's structure. Definitely going to use it
         | for any DT slides I have to make.
        
         | prionassembly wrote:
         | People can also follow additive scores. This is what e.g. the
         | DSM does.
         | 
         | A spreadsheet-like printed table where you mark items and sum
         | scores in your head is probably easier to follow than a
         | similarly-powered decision tree. Of course, you can't guarantee
         | that a linear decision boundary exists, but in case there is
         | one, the standard tools (Gauss-Markov/FWL theorems, p-values,
         | etc.) are much, much more robust than CART or C4.5.
        
         | __mharrison__ wrote:
         | I had a weird experience recently. We tore out our deck during
         | Covid lockdown and I had a rusty nail puncture my skin. The
         | doctor who treated it pulled out a sheet that was essentially a
         | decision tree that determined that after cleaning the wound I
         | just needed to be sent home with a lollipop. (Recent tetanus
         | shot was sufficient.)
         | 
         | The strange part was when the doctor showed me the tree and
         | asked me if I agreed... My response was "You're the doctor, you
         | tell me?!"
        
         | riedel wrote:
         | Not really. If we are doing data science for many companies and
         | explainability is an aspect we will likely go for decision
         | trees if the lift for advanced models is minor anyways. We use
         | this (not quite as pretty for visualization but extremely
         | useful to get a grasp if the tree model):
         | https://github.com/parrt/dtreeviz
        
       | carabiner wrote:
       | What I'd like is some clever way of visualizing a randomforest in
       | its entirety, rather than just showing the individual trees as
       | they do in
       | https://gitlab.tue.nl/20040367/pybaobab/-/raw/main/images/ra....
       | I have no idea how to go about doing this.
        
       | __mharrison__ wrote:
       | This is really pretty. Thanks for sharing, I will probably
       | leverage this in my training!
        
       | morelandjs wrote:
       | This looks incredible! Excited to try this!
        
       | vletal wrote:
       | So was the Little Prince applying daily regularization to the
       | baobabs on his planet?
        
       | ktpsns wrote:
       | Nice! For anybody who does not have access to the referenced IEEE
       | paper (such as me), here it is on the website of its authors:
       | https://pure.tue.nl/ws/portalfiles/portal/3522084/6724346112...
        
         | harabat wrote:
         | And the GitLab repo: https://gitlab.tue.nl/20040367/pybaobab
        
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       (page generated 2021-12-02 23:02 UTC)