[HN Gopher] Human-Learn: Draw Machine Learning Models
___________________________________________________________________
Human-Learn: Draw Machine Learning Models
Author : polm23
Score : 44 points
Date : 2021-01-30 06:13 UTC (1 days ago)
(HTM) web link (koaning.github.io)
(TXT) w3m dump (koaning.github.io)
| refactor_master wrote:
| If your boundaries are so easily visualized that you can draw a
| circle around them, then why not use a fully automatic(tm)
| decision tree, and let the computer do the hard work?
|
| This is like the fits people drew on paper before computers were
| available.
| Der_Einzige wrote:
| humans provide accidental regularization - and a human made
| decision boundary can have further fine-tuning by an expert.
| This is important related to things like calibrating
| probabilities, which decision trees do very poorly on even if
| they get good scores on the metric they're trained on.
|
| Even better if you have a generative model which can
| "hallucinate" parts of the decision boundary which don't have
| points. Now you can "probe" your model and have a human
| intervene if they think parts of the decision boundary are
| wrong
|
| I suppose a human can modify a decision tree as well - but in
| practice human generated rules may be better for some domains.
| zitterbewegung wrote:
| This sounds neat as a type of low code way to make Machine
| Learning models but how much different is this than using pandas?
| [deleted]
| yters wrote:
| Absolutely brilliant. Ever since taking ML classes in MSc and
| then again in PhD I have wondered why no machine learning
| approach ever applies such an obvious technique. And most real
| world datasets are probably adequately addressed with this
| approach, and everyone regardless of technical background can
| understand it. ML no longer has to be an inscrutable black box
| mystery! My hat is off to you sir!
| iujjkfjdkkdkf wrote:
| This is a neat demo, but at the same time, what any of the more
| sophisticated ML and deep learning techniques do is effectively
| create that picture for you. If you're at the stage of seeing the
| data like that, the hard work has been done. Being able to
| project the data into a latent space where classes can be
| separated is what makes modern ML so valuable.
| Der_Einzige wrote:
| PCA is so fast that it can run on 100 million data-points and
| give you results in minutes with the proper implementation of
| it.
|
| If you can find a way to project that new data quickly, and if
| the decision boundries are instantly recognizable to us humans
| - one can draw the boundary faster than a model could generate
| it given the data. The human drawn boundary has bonus
| regularization built right in too (because "sub-optimal"
| boundaries on the training data may generalize better)
| scribu wrote:
| This is a really interesting project. It's a collection of
| utilities for constructing rule-based models, as opposed to
| statistical models (i.e. ML).
|
| I can see myself using it for exploratory analysis and
| constructing baseline models.
|
| As for the "let's just draw the model" idea, it gets complicated
| as soon as you're dealing with more than one input parameter. The
| author has some workarounds, it looks like:
|
| https://koaning.github.io/human-learn/guide/drawing-classifi...
| Der_Einzige wrote:
| Still not sure why this repo doesn't implement dimensionality
| reduction techiques. Seems like a huge oversight since that's
| what you need for doing this on high dimensional data.
|
| "Let's draw the model" works in a PCA or UMAP space if your
| boundaries are already pretty good. I'm surprised that we
| haven't seen more machine teaching tools like this...
___________________________________________________________________
(page generated 2021-01-31 23:01 UTC)