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Transform Data by Example [video]

microsoft.com

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Re: Transform Data by Example [video]

#71

Post by jmmcd »

Houshalter wrote: post #60
randomsearch wrote: post #45

Earlier quoted context omitted.

> This might be a dumb question, but let's say that for whatever reason on a specific problem it's much easier to train a neural network that generalizes well than a decision tree. Why not train the network, then build an equivalent decision tree that just tries to reproduce the network's output? When building the tree from the network, overfitting would not be a concern. In fact, you'd want it to overfit. You haven'…

This solves the problem of interpretability. You can't interpret the weights of a neural network, but you can easily follow along a decision tree and see if it's doing what you want. Actually that's somewhat less true for big decision trees. But the general point is that you can train interpretable models to mimic the output of uninterpretable black boxes. The biggest issue is that decision trees only work for data w…

No, it doesn't make sense. The training data (inputs and NN-predicted outputs) that you're feeding into the DT is at best the same as the training data (inputs and desired outputs) you had originally.

Re: Transform Data by Example [video]

#72

Post by [deleted] »

Houshalter wrote: post #60
randomsearch wrote: post #45

Earlier quoted context omitted.

> This might be a dumb question, but let's say that for whatever reason on a specific problem it's much easier to train a neural network that generalizes well than a decision tree. Why not train the network, then build an equivalent decision tree that just tries to reproduce the network's output? When building the tree from the network, overfitting would not be a concern. In fact, you'd want it to overfit. You haven'…

This solves the problem of interpretability. You can't interpret the weights of a neural network, but you can easily follow along a decision tree and see if it's doing what you want. Actually that's somewhat less true for big decision trees. But the general point is that you can train interpretable models to mimic the output of uninterpretable black boxes. The biggest issue is that decision trees only work for data w…

[deleted]

Re: Transform Data by Example [video]

#73

Post by Houshalter »

jmmcd wrote: post #71
Houshalter wrote: post #60

Earlier quoted context omitted.

This solves the problem of interpretability. You can't interpret the weights of a neural network, but you can easily follow along a decision tree and see if it's doing what you want. Actually that's somewhat less true for big decision trees. But the general point is that you can train interpretable models to mimic the output of uninterpretable black boxes. The biggest issue is that decision trees only work for data w…

No, it doesn't make sense. The training data (inputs and NN-predicted outputs) that you're feeding into the DT is at best the same as the training data (inputs and desired outputs) you had originally.

You can generate infinite training data with the NN by feeding in random inputs and seeing what outputs it gives. You can then train whatever model you want on it without concern for overfitting.

But more importantly, the decision tree will model the behavior of the NN, not necessarily the original data. Which is what you want, if your goal is to understand what function the NN has learned.

Re: Transform Data by Example [video]

#78

Post by dandare »

ktamura wrote: post #2

This is a great product idea. If you ask any Excel power users, by far the most time-consuming and hard-to-automate task is text and date manipulation. The beauty of this product is that its adoption strategy is baked into the product itself: I'd share this with all Excel user friends of mine because I want the algorithm to get smarter, and I might even learn a bit of C# myself so that I can contribute and scratch my…

Slightly different use case but still very useful for data manipulation is the OpenRefine (formerly Google Refine) https://www.youtube.com/watch?v=B70J_H_zAWM

Re: Transform Data by Example [video]

#79

Post by TuringTest »

teddyh wrote: post #66
tonmoy wrote: post #37

Earlier quoted context omitted.

MS had been experimenting with this for a while[1]. They even included this in Excel 2013 as "FlashFill". It does not use any NN/ML at all. It uses "program synthesis", which by definition can tell you exactly what "program" it has synthesized to convert you data. In fact in you example it would not cheat, rather leave the string unchanged as explained in the paper. [1] https://www.microsoft.com/en-us/research/public…

But maybe I did want it to remove the middle character! Using my training data, there’s no way for the system to actually know for sure what I meant. There is also no way (in general) for it to detect “outliers” and ask me about them, because there is no good way to know what is an outlier and what is not.

This is why this kind of software should have an interactive 'feedback' function that allows the user to select among several, equally likely rules.

The experimental Lapis editor[1] did exactly this, by the way.

[1] https://en.wikipedia.org/wiki/Lapis_(text_editor)

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