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> Classification models break down to regression problems under the hood, but regression metrics are not good tools to evaluate the efficacy of classification m

> Classification models break down to regression problems under the hood, but regression metrics are not good tools to evaluate the efficacy of classification models. You're simply wrong. Regression is a tried and true classification technique. Posting personal and baseless assertions don't refute that. I mean, pick up pretty much any textbook on supervised and unsupervised learning. You always end up with an approach which boils down to having training data, put together a trial function, apply a minimizer to fit trial functions to training data, and evaluate the resulting model by running trial data through it. Fitting trial functions to training data has a name: regression. Minimum squares has a very precise interpretation both in linear models and in probability. There is no way around it.

jncfhnb 2y ago
In most modern ML circles “regression” analysis refers to the prediction of continuous variables whereas “classification” refers to the estimation across discrete outputs. This is true even for “logistic regression”. What I said was that regression metrics are not good for evaluating usefulness in classification problems. An R2 of 0.01. The fact that there are mitigating circumstances to justify why this might not be the case is not evidence that R2 is still a good thing to use. It’s actually evidence of the opposite. With classification problems we are concerned more often with the ordinality of estimated probabilities vs outcomes and/or the calibration of a model. > Posting personal and baseless assertions don't refute that. Your mom didn’t refute me either.
- chipdart 2y ago
  > In most modern ML circles “regression” analysis refers to the prediction of continuous variables (...) You're voicing very opinionated takes while showing considerable ignorance on the topic. Classification problems are solved with trial functions adjusted to the training set through regression. These trial functions,once fitted, represent membership functions. They are essentially interpolation functions that, say, converge to 1 when close to elements of the training set of a specific class, and 0 for members of all other classes. In grey areas where elements of the training set are sparsely distributed, these membership functions can output values in the middle, because they are interpolating. That's literally data mining 101. > What I said was that regression metrics are not good for evaluating usefulness in classification problems. That's simply not true. It's like saying models that do not fit the data are good approximations of the data. Another way to put it is praising the accuracy of a broken clock because it's spot on two times a day. > The fact that there are mitigating circumstances to justify why this might not be the case is not evidence that R2 is still a good thing to use. It’s actually evidence of the opposite. I don't think you have an adequate grasp on the subject, neither classification problems nor linear models. Therefore, your personal baseless assertions don't mean anything nor bring any value to the discussion.
  - jncfhnb 2y ago
    I’m a data scientist that builds ML models for a living. You’ve twice now tried to explain how fitting a model works in response to me stating that regression “metrics” are not well suited to describing classification “efficacy”. If anyone is making a basic mistake here it seems to be you failing to understand the difference between a “regression model” like a linear regression, and a “regression metric” like R2. I’ve stated my semantic meaning of regression vs classification. You can Google this to see it is not a fringe view. It’s been standard for over a decade. Eg https://math.stackexchange.com/questions/141381/what-is-the-difference-between-regression-and-classification https://math.stackexchange.com/questions/141381/what-is-the-... > Another way to put it is praising the accuracy of a broken clock because it's spot on two times a day. Accuracy is a classification metric kiddo Here’s an introductory Wikipedia article if you would like to learn more about what metrics are appropriate for binary classification evaluations: https://en.m.wikipedia.org/wiki/Evaluation_of_binary_classifiers https://en.m.wikipedia.org/wiki/Evaluation_of_binary_classif...

jncfhnb 2y ago

In most modern ML circles “regression” analysis refers to the prediction of continuous variables whereas “classification” refers to the estimation across discrete outputs. This is true even for “logistic regression”. What I said was that regression metrics are not good for evaluating usefulness in classification problems. An R2 of 0.01. The fact that there are mitigating circumstances to justify why this might not be the case is not evidence that R2 is still a good thing to use. It’s actually evidence of the opposite. With classification problems we are concerned more often with the ordinality of estimated probabilities vs outcomes and/or the calibration of a model. > Posting personal and baseless assertions don't refute that. Your mom didn’t refute me either.

chipdart 2y ago
> In most modern ML circles “regression” analysis refers to the prediction of continuous variables (...) You're voicing very opinionated takes while showing considerable ignorance on the topic. Classification problems are solved with trial functions adjusted to the training set through regression. These trial functions,once fitted, represent membership functions. They are essentially interpolation functions that, say, converge to 1 when close to elements of the training set of a specific class, and 0 for members of all other classes. In grey areas where elements of the training set are sparsely distributed, these membership functions can output values in the middle, because they are interpolating. That's literally data mining 101. > What I said was that regression metrics are not good for evaluating usefulness in classification problems. That's simply not true. It's like saying models that do not fit the data are good approximations of the data. Another way to put it is praising the accuracy of a broken clock because it's spot on two times a day. > The fact that there are mitigating circumstances to justify why this might not be the case is not evidence that R2 is still a good thing to use. It’s actually evidence of the opposite. I don't think you have an adequate grasp on the subject, neither classification problems nor linear models. Therefore, your personal baseless assertions don't mean anything nor bring any value to the discussion.
- jncfhnb 2y ago
  I’m a data scientist that builds ML models for a living. You’ve twice now tried to explain how fitting a model works in response to me stating that regression “metrics” are not well suited to describing classification “efficacy”. If anyone is making a basic mistake here it seems to be you failing to understand the difference between a “regression model” like a linear regression, and a “regression metric” like R2. I’ve stated my semantic meaning of regression vs classification. You can Google this to see it is not a fringe view. It’s been standard for over a decade. Eg https://math.stackexchange.com/questions/141381/what-is-the-difference-between-regression-and-classification https://math.stackexchange.com/questions/141381/what-is-the-... > Another way to put it is praising the accuracy of a broken clock because it's spot on two times a day. Accuracy is a classification metric kiddo Here’s an introductory Wikipedia article if you would like to learn more about what metrics are appropriate for binary classification evaluations: https://en.m.wikipedia.org/wiki/Evaluation_of_binary_classifiers https://en.m.wikipedia.org/wiki/Evaluation_of_binary_classif...