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I think interpretability is much more correlated with model size than model type. Small neural net is much more interpretable than decision trees with thousand

by Scea91 8y ago

I think interpretability is much more correlated with model size than model type. Small neural net is much more interpretable than decision trees with thousands of nodes.

taeric 8y ago
I challenge that last statement. Have there been any neural nets that actually have a solid interpretability? Usually those are more on the lines of effectiveness in training and validation data. With no real clue as to what the driving features were.
- Scea91 8y ago
  https://distill.pub/2018/building-blocks/ https://distill.pub/2018/building-blocks/ This seems pretty good to me. And the nets are not exactly as small as I meant. I work with random forests and build forest which have more than 80000 nodes per tree. Other than some basic computation of feature importance, it is a black box on the same scale as modern neural nets, maybe even worse.
  - taeric 8y ago
    Thanks for the link. Will take me some time to digest it. Last time I looked at one of these, it was less informative than I'd care to admit. I want to know why a classifier found a vase. The answer is typically some form of "because it was able to see the vase." Would be more interesting to see a classifier that predicts something is going to happen. A predictor that can predict a person is about to step off of a curb, for example. Is it pose of the person? Did it require seeing multiple frames of the person, such that it was an inertia preditor? So, yes, comparing things to gigantic trees can make things tough. But I thought that was the beauty of boosting and building up smaller trees. Most of them are usually more interpretable than you might expect.
    - Scea91 8y ago
      > So, yes, comparing things to gigantic trees can make things tough. But I thought that was the beauty of boosting and building up smaller trees. Most of them are usually more interpretable than you might expect. Regarding your last paragraph, I found this paper https://arxiv.org/pdf/1504.07676.pdf https://arxiv.org/pdf/1504.07676.pdf worth reading. Excerpt from the abstract: "We conclude that boosting should be used like random forests: with large decision trees and without direct regularization or early stopping."
- srean 8y ago
  > Small neural net is much more interpretable than decision trees with thousands of nodes. Yes. Example a simple logistic regression model

by Scea91 8y ago

I think interpretability is much more correlated with model size than model type. Small neural net is much more interpretable than decision trees with thousands of nodes.

taeric 8y ago

I challenge that last statement. Have there been any neural nets that actually have a solid interpretability? Usually those are more on the lines of effectiveness in training and validation data. With no real clue as to what the driving features were.

Scea91 8y ago
https://distill.pub/2018/building-blocks/ https://distill.pub/2018/building-blocks/ This seems pretty good to me. And the nets are not exactly as small as I meant. I work with random forests and build forest which have more than 80000 nodes per tree. Other than some basic computation of feature importance, it is a black box on the same scale as modern neural nets, maybe even worse.
- taeric 8y ago
  Thanks for the link. Will take me some time to digest it. Last time I looked at one of these, it was less informative than I'd care to admit. I want to know why a classifier found a vase. The answer is typically some form of "because it was able to see the vase." Would be more interesting to see a classifier that predicts something is going to happen. A predictor that can predict a person is about to step off of a curb, for example. Is it pose of the person? Did it require seeing multiple frames of the person, such that it was an inertia preditor? So, yes, comparing things to gigantic trees can make things tough. But I thought that was the beauty of boosting and building up smaller trees. Most of them are usually more interpretable than you might expect.
  - Scea91 8y ago
    > So, yes, comparing things to gigantic trees can make things tough. But I thought that was the beauty of boosting and building up smaller trees. Most of them are usually more interpretable than you might expect. Regarding your last paragraph, I found this paper https://arxiv.org/pdf/1504.07676.pdf https://arxiv.org/pdf/1504.07676.pdf worth reading. Excerpt from the abstract: "We conclude that boosting should be used like random forests: with large decision trees and without direct regularization or early stopping."
srean 8y ago
> Small neural net is much more interpretable than decision trees with thousands of nodes. Yes. Example a simple logistic regression model