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How do we differentiate between econometrics and machine learning? Logistic regression seems like it fits into econometrics better than machine learning to me. There's no regularization. I guess there's gradient descent which can be seen as more machine learning. In the end it's semantics of course, still an interesting distinction.
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I don’t understand the comment about Leela. Why isn’t own move prediction deterministic?
Because Leela (like fastchess mentioned above) has two parts: A neural network predicting good moves, and a tree search exploring the moves suggested and evaluating the resulting positions (with a second net). If the prediction (policy) net had a 100% accuracy, you wouldn't need the tree search part at all.
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Well, what do you define as machine learning? Logistic regression is clearly a classifier. And you need data to train it. So it's a supervised learning algorithm.
I'm trying to have a conversation so I can figure it out. Pretty confident that being a classifier does not make it machine learning, econometrics has classifiers too. Econometric models also need data to train them, so I'm not sure your second point is helpful either. Unless you're claiming the difference is nothing but whether the model is used by an economist.
Earlier quoted context omitted.
Because Leela (like fastchess mentioned above) has two parts: A neural network predicting good moves, and a tree search exploring the moves suggested and evaluating the resulting positions (with a second net). If the prediction (policy) net had a 100% accuracy, you wouldn't need the tree search part at all.
You haven’t mentioned any nondeterministic behaviour, therefore Leela is supposed to predict it’s own moves with a 100% accuracy.
How do we differentiate between econometrics and machine learning? Logistic regression seems like it fits into econometrics better than machine learning to me. There's no regularization. I guess there's gradient descent which can be seen as more machine learning. In the end it's semantics of course, still an interesting distinction.
Machine learning is typically defined as a way to enable computers to learn from data to accomplish tasks, without explicitly telling them how.
Both fields can use logistic regression, regularization, and gradient descent to accomplish their goals, so in that sense there's no distinction.
But IMO there is a difference in their primary intention: econometrics typically focuses on inference about relationships, machine learning typically focuses on predictive accuracy. That's not to say that econometrics doesn't consider predictive accuracy, or that machine learning doesn't consider inference, but it's usually not their primary concern.
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It's better to think of linear regression and logistic regression as special cases of the Generalized Linear Model (GLM). In that framework, they are literally the same model with different "settings" - Gaussian vs Bernoulli distribution.
I have to disagree with you. While assuming Gaussian disturbance terms results in a linear regression, the linear regression framework is more general. It makes no assumptions about the distribution of the disturbance terms. Instead, it merely restricts the variance to be constant over all values of the response variable.
Linear regression is extra-special because it's a special case of several different frameworks and model classes.
I should have written that it's better (in my opinion) to think of logistic regression in the context of GLMs, at least while you're learning.
Edit: yes logistic regression is a special case of regression with a different loss function. But it's not nearly "as special" as linear regression.
How do we differentiate between econometrics and machine learning? Logistic regression seems like it fits into econometrics better than machine learning to me. There's no regularization. I guess there's gradient descent which can be seen as more machine learning. In the end it's semantics of course, still an interesting distinction.
Econometrics is the application of statistical techniques on economics-related problems, typically to understand relationships between economic phenomena (e.g. income) and things that might be associated with it (e.g. education). Machine learning is typically defined as a way to enable computers to learn from data to accomplish tasks, without explicitly telling them how. Both fields can use logistic regression, regul…
Earlier quoted context omitted.
I don’t understand the comment about Leela. Why isn’t own move prediction deterministic?
Because Leela (like fastchess mentioned above) has two parts: A neural network predicting good moves, and a tree search exploring the moves suggested and evaluating the resulting positions (with a second net). If the prediction (policy) net had a 100% accuracy, you wouldn't need the tree search part at all.
Part one of Leela ranks several chess moves. Part two picks among those.
60% of the time part 2 chooses the #1 ranked move.
Very interesting seeing people in the comments debate what is a very basic thing taught in any stats/econometrics class. The idea behind binary regression ( Y is 0 or 1) is that you use a latent variable Y* = beta X + epsilon. X is the matrix of indenpent variables, beta is the vector of coefficients and epsilon is an error term that sums the rest of what X can't explain. Y thus becomes 1 if Y* >0 and 0 otherwise. Se…
- this is a very basic thing
- that's all there is to it
because although it is a basic thing to you, it's not a basic thing to someone who hasn't spent the same time studying all the concepts beforehand.
This is generally why you have to take a class to grok stats rather than just read some reference material and definitions.
It's similar to programming environments when the senior dev says some thing that requires a lot of context is just really simple.
Monads are Simply Just monoids in the category of endofunctors, after all, they are really Simple, that's all there is to it! (what the subject hears: This is so simple to me, why aren't you smart enough to get this simple concept? You should know this already—I shouldn't even have to make this comment! Why haven't you been properly educated, like I have? )
Very interesting seeing people in the comments debate what is a very basic thing taught in any stats/econometrics class. The idea behind binary regression ( Y is 0 or 1) is that you use a latent variable Y* = beta X + epsilon. X is the matrix of indenpent variables, beta is the vector of coefficients and epsilon is an error term that sums the rest of what X can't explain. Y thus becomes 1 if Y* >0 and 0 otherwise. Se…
(i.e. > 0.5 equals 1, and This seems to be a "rarely-questioned assumption".
Is there a reason why this is considered reasonable? And is there a name for the cut-off (i.e., if I were to want to change the cut-off, what keyword should I search for inside the software's manual?)?