Timeline for The Two Cultures: statistics vs. machine learning?
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 22 at 8:26 | comment | added | Johan | I opened a question about the notions of estimator bias and variance/efficiency. For those of you who touched on in here, please consider responding to my question (that would be helpful): stats.stackexchange.com/questions/653158/… | |
| Aug 16, 2016 at 18:12 | history | edited | doug | CC BY-SA 3.0 |
added clarifidation to sentence on logistic regression
|
| Aug 16, 2016 at 17:45 | comment | added | Firebug | Also, never heard the machine argument. In ML there's classification, regression, clustering, etc. The learners are just that, learners, and are apt for different tasks. In general, only SVMs are related algorithms (LSSVM, RVM, IVM, TSVM, etc) are called machines. | |
| Aug 16, 2016 at 17:43 | comment | added | Firebug | "logistic regression on the other hand, returns a class labels": that isn't quite right is it? Logistic regression returns probability estimates, not labels. EDIT: I see someone said the same thing. | |
| Feb 29, 2016 at 22:03 | comment | added | random_guy | The statement "logistic regression on the other hand, returns a class labels." is wrong. Logistic regression returns continues values in $[0, 1]$ that are estimates for the probability to belong to the class coded as $1$. | |
| Nov 24, 2014 at 10:13 | comment | added | Danica | Many of these aren't consistent with the usage I've seen in the ML community. Both types of kernels are in wide use (though Hilbert space kernels being more common), "machine" is basically only used for SVMs (as iliasfl notes), and "bias" usually means $\mathbb{E}[\hat{X} - X]$ (perhaps conditioned on something) which is not the same thing as an intercept. | |
| Nov 3, 2014 at 5:25 | comment | added | iliasfl | I think the term "machine" in SVMs should be attributed to the personal taste of Vladimir Vapnic. Nowadays, I don't think it is not used to name any other classifier. | |
| Oct 31, 2013 at 2:42 | comment | added | Silverfish | (@Joris Or even if you don't know it! Sounds trite, but just figuring out if there's bias can be a considerable practical problem. From the data alone, how sure can you be that an estimated regression parameter is free of omitted variable bias?) It's a common misconception that bias is a feature of the data, not a property of an estimator; I wonder if it stems from non-technical usage like "that survey is biased!" Statisticians also aren't always consistent about terms like "error": mean square error (of an estimator) includes a bias-squared component, so that "error" isn't "purely random". | |
| Sep 30, 2012 at 6:36 | history | edited | doug | CC BY-SA 3.0 |
minor edits for readability
|
| Sep 8, 2010 at 21:30 | comment | added | Joris Meys | In statistics, bias is not the same as error. Error is purely random, bias is not. You have bias when you know that the expected value of your estimate is not equal to the true value. | |
| Aug 9, 2010 at 10:12 | history | answered | doug | CC BY-SA 2.5 |