4 ms·

I've never heard supervised learning referred to as model-based learning.

by nkozyra 10y ago

yummyfajitas 10y ago
This isn't a book about supervised learning, from what I can tell. Based on my reading of the murder mystery and the skill assessment, it's about defining models based on your understanding of the underlying system and then fitting them to the data. This is a lot closer to classical statistics than machine learning.
- kristjankalm 10y ago
  rather than downvoting i'm actually curious why you think unsupervised learning is not ML? there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI
  - huac 10y ago
    I think yummyfajita's point is that a traditional statistics approach begins with some understanding of the system being modeled, and that you create a model using that understanding. There is usually a high focus on parsimoniousness and explainability, while in ML/AI, you don't really care what the underlying model is or how the model comes to a particular conclusion. The focus is on accuracy at the expense of explainability.
  - povik 10y ago
    I am afraid you misunderstood. OP's point isn't that it's unsupervised learning, hence not ML. His point is that it's no learning at all. IMHO.
  - yummyfajitas 10y ago
    To me at least, the major distinction between "classical statistics" and "machine learning" is that machine learning" strives to work independently of the underlying distribution while classical statistics tries to model it. I.e., a statistician doing linear regression assumes that reality is linear (or at least differentiable) in the region of interest. A convergence proof of linear regression will use this assumption. A machine learning practitioner does NOT assume reality actually has a random forest out there in the world somewhere, and as a result needs to prove far more general (and less accurate) convergence results for the random forest. From what I can tell, this book falls into the former category.
    - xapata 10y ago
      > assumes that reality is linear The assumption is that a particular relationship is reasonable to model as if it were linear. No one believes reality is strictly linear. I've read your posts enough to believe you know how linear regression works. I'm criticizing your comment because it encourages a misunderstanding of traditional statistics as having nonsensical assumptions.
      - yummyfajitas 10y ago
        Out of curiosity, was my caveat "(or at least differentiable) in the region of interest" insufficient for that purpose? I certainly didn't mean to imply that statistics has unreasonable assumptions. Merely that it tends to have stronger assumptions - and more accurate results - than machine learning. Personally I'm a huge fan of classical statistics and think it's currently underappreciated.
        xapata 10y ago
        The caveat doesn't work for a technical reason and a more important practical reason. Most relationships, even ones that aren't proper functions, can be transformed into a linear model. An absolute value function is non-differentiable for one value of the input, but it'd be perfectly fine to model with linear regression. More importantly, the audience I worry about isn't the type to pay attention to parenthetical notes using jargon. Linear is somewhat accessible jargon, but differentiable is less so. I'm not claiming that I write clearly, but I aim to write such that I don't need caveats.
        jefft255 10y ago
        Yes it really is underappreciated. As quoted by other comments, "Most businesses think they need advanced ML and really what they need is linear regression and cleaned up data". A significant portion of businesses currently investing millions in ML should basically hire a couple of statisticians and get over it.
        xapata 10y ago
rm999 10y ago
The introduction clarifies what the authors mean. In this context "model" isn't about implementing a supervised model, it's about "modeling" your problem to build a bespoke algorithm that closely matches the problem. Unsupervised methods like clustering would probably fit in here too. I haven't read much of this early access book yet, but I'd give the authors a lot of benefit of the doubt. Christopher Bishop wrote one of my favorite machine learning books (I read it after my graduate study in machine learning and it filled in a lott of the gaps): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 https://www.amazon.com/Pattern-Recognition-Learning-Informat...
- brudgers 10y ago
  From the Hacker News guidelines: Please don't insinuate that someone hasn't read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that." It is possible to edit the comment to remove the phrase if you wish.
  - rm999 10y ago
    It was an honest question, not snark (passive aggressiveness is not my style). The introduction is kind of hidden on the page, and clarifies the meaning of "model" in this context. Otherwise, GP is correct that "model" is often used to mean a supervised model, and that people generally call it "supervised learning", not "model-based learning".
    - brudgers 10y ago
      I'm glad it was an honest question. Editing the comment is an option. I think the guideline exists because even as an honest question it does not add anything to the comment and at best an answer doesn't change anything and at worst it detracts from meaningful dialog. One feature of this particular guideline is that it provides an alternative phrasing that is likely to avoid misinterpretation.
      - rm999 10y ago
Cybiote 10y ago
As rm999 points out, this book is vastly more useful than limiting distinctions such as supervised/unsupervised learning (what happens in brains is learning while predicting, which is not completely well captured by that delineation, nor even fully by reinforcement learning). This book will provide a set of skills which will age far better than if it had been specific to some machine learning framework or ideas. It's one of the best I've seen on reasoning probabilistically, bayesian networks, graphical models and probabilistic programming generally. It also teaches the core of the involved algorithms. These skills will be important going forward as we seek to implement ever more brain like systems (and better). The knowledge will also carry over to gaussian processes (which are a subset really) and the more future proof generative deep learning ideas. It also teaches how to reason about your problem and diagnose machine learning systems. Whether you're designing features, trying to figure out how to make a research paper work in real life, or are one of the rare people capable of coming up with deep learning architectures, what the book teaches will be indispensable to you.
llasram 10y ago
My take from the introduction is that the books is going to mostly be about probabilistic graphical models (PGMs). I look forward to reading this book when finished and hope they find success with this presentation of the core ideas. As a practitioner I see a fair amount of "I have a hammer; now I just need this problem to be a nail" type thinking with regard to using off-the-shelf techniques. In the intro to this book the authors have an example with Kalman filters. A similar example is how Latent Dirichlet Allocation (LDA) is treated by different communities. In a certain chunk of the CS-dominated topic-modeling literature and in the data science blogosphere LDA is this recieved atomic technique; a black-box tool for modeling documents. In the Stan manual, it is one fairly boring example of a mixture model, only worth talking about explicitly because so many people ask about it.

I've never heard supervised learning referred to as model-based learning.

by nkozyra 10y ago

I've never heard supervised learning referred to as model-based learning.

This isn't a book about supervised learning, from what I can tell. Based on my reading of the murder mystery and the skill assessment, it's about defining models based on your understanding of the underlying system and then fitting them to the data. This is a lot closer to classical statistics than machine learning.

kristjankalm 10y ago
rather than downvoting i'm actually curious why you think unsupervised learning is not ML? there'd be so much less noise in these comments/discussions if we just did away with vague and illdefined labels such as ML or AI
- huac 10y ago
  I think yummyfajita's point is that a traditional statistics approach begins with some understanding of the system being modeled, and that you create a model using that understanding. There is usually a high focus on parsimoniousness and explainability, while in ML/AI, you don't really care what the underlying model is or how the model comes to a particular conclusion. The focus is on accuracy at the expense of explainability.
- povik 10y ago
  I am afraid you misunderstood. OP's point isn't that it's unsupervised learning, hence not ML. His point is that it's no learning at all. IMHO.
- yummyfajitas 10y ago
  To me at least, the major distinction between "classical statistics" and "machine learning" is that machine learning" strives to work independently of the underlying distribution while classical statistics tries to model it. I.e., a statistician doing linear regression assumes that reality is linear (or at least differentiable) in the region of interest. A convergence proof of linear regression will use this assumption. A machine learning practitioner does NOT assume reality actually has a random forest out there in the world somewhere, and as a result needs to prove far more general (and less accurate) convergence results for the random forest. From what I can tell, this book falls into the former category.
  - xapata 10y ago
    > assumes that reality is linear The assumption is that a particular relationship is reasonable to model as if it were linear. No one believes reality is strictly linear. I've read your posts enough to believe you know how linear regression works. I'm criticizing your comment because it encourages a misunderstanding of traditional statistics as having nonsensical assumptions.
    - yummyfajitas 10y ago
      Out of curiosity, was my caveat "(or at least differentiable) in the region of interest" insufficient for that purpose? I certainly didn't mean to imply that statistics has unreasonable assumptions. Merely that it tends to have stronger assumptions - and more accurate results - than machine learning. Personally I'm a huge fan of classical statistics and think it's currently underappreciated.
      - xapata 10y ago
        The caveat doesn't work for a technical reason and a more important practical reason. Most relationships, even ones that aren't proper functions, can be transformed into a linear model. An absolute value function is non-differentiable for one value of the input, but it'd be perfectly fine to model with linear regression. More importantly, the audience I worry about isn't the type to pay attention to parenthetical notes using jargon. Linear is somewhat accessible jargon, but differentiable is less so. I'm not claiming that I write clearly, but I aim to write such that I don't need caveats.
      - jefft255 10y ago
        Yes it really is underappreciated. As quoted by other comments, "Most businesses think they need advanced ML and really what they need is linear regression and cleaned up data". A significant portion of businesses currently investing millions in ML should basically hire a couple of statisticians and get over it.
        xapata 10y ago

rm999 10y ago

The introduction clarifies what the authors mean. In this context "model" isn't about implementing a supervised model, it's about "modeling" your problem to build a bespoke algorithm that closely matches the problem. Unsupervised methods like clustering would probably fit in here too. I haven't read much of this early access book yet, but I'd give the authors a lot of benefit of the doubt. Christopher Bishop wrote one of my favorite machine learning books (I read it after my graduate study in machine learning and it filled in a lott of the gaps): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 https://www.amazon.com/Pattern-Recognition-Learning-Informat...

brudgers 10y ago
From the Hacker News guidelines: Please don't insinuate that someone hasn't read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that." It is possible to edit the comment to remove the phrase if you wish.
- rm999 10y ago
  It was an honest question, not snark (passive aggressiveness is not my style). The introduction is kind of hidden on the page, and clarifies the meaning of "model" in this context. Otherwise, GP is correct that "model" is often used to mean a supervised model, and that people generally call it "supervised learning", not "model-based learning".
  - brudgers 10y ago
    I'm glad it was an honest question. Editing the comment is an option. I think the guideline exists because even as an honest question it does not add anything to the comment and at best an answer doesn't change anything and at worst it detracts from meaningful dialog. One feature of this particular guideline is that it provides an alternative phrasing that is likely to avoid misinterpretation.
    - rm999 10y ago

Cybiote 10y ago

As rm999 points out, this book is vastly more useful than limiting distinctions such as supervised/unsupervised learning (what happens in brains is learning while predicting, which is not completely well captured by that delineation, nor even fully by reinforcement learning). This book will provide a set of skills which will age far better than if it had been specific to some machine learning framework or ideas. It's one of the best I've seen on reasoning probabilistically, bayesian networks, graphical models and probabilistic programming generally. It also teaches the core of the involved algorithms. These skills will be important going forward as we seek to implement ever more brain like systems (and better). The knowledge will also carry over to gaussian processes (which are a subset really) and the more future proof generative deep learning ideas. It also teaches how to reason about your problem and diagnose machine learning systems. Whether you're designing features, trying to figure out how to make a research paper work in real life, or are one of the rare people capable of coming up with deep learning architectures, what the book teaches will be indispensable to you.

llasram 10y ago

My take from the introduction is that the books is going to mostly be about probabilistic graphical models (PGMs). I look forward to reading this book when finished and hope they find success with this presentation of the core ideas. As a practitioner I see a fair amount of "I have a hammer; now I just need this problem to be a nail" type thinking with regard to using off-the-shelf techniques. In the intro to this book the authors have an example with Kalman filters. A similar example is how Latent Dirichlet Allocation (LDA) is treated by different communities. In a certain chunk of the CS-dominated topic-modeling literature and in the data science blogosphere LDA is this recieved atomic technique; a black-box tool for modeling documents. In the Stan manual, it is one fairly boring example of a mixture model, only worth talking about explicitly because so many people ask about it.