Timeline for Why does linear SVM perform better than deep learning techniques on my problem?
Current License: CC BY-SA 4.0
8 events
| when toggle format | what | by | license | comment | |
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| Apr 10, 2023 at 12:39 | comment | added | Dikran Marsupial | ... it also gives a computationally inexpensive means of checking your pre-processing and evaluation framework/pipeline, so it is well worth doing. | |
| Apr 10, 2023 at 12:37 | comment | added | Dikran Marsupial | "Isn't deep learning supposed to surpass machine learning models on such complex task like text classification?" no. Neural network research has gone through several hype-bust cycles, and it is this sort of hyperbolic claim that causes the cycles. If a problem has an optimal decision boundary that happens to be a hyperplane, a linear classifier is bound to do better than a complex non-linear model as there is simply less to go wrong (e.g. bad local minima to avoid) - estimation is easier. Always try simple classifiers as a baseline whenever you want to apply a complex non-linear model. | |
| Mar 25, 2022 at 18:12 | comment | added | Sycorax♦ | Accuracy of 0.538 versus 0.528 doesn't suggest that either model is doing terribly well. Perhaps the problem is very challenging, or the choice of features are not very informative. Or none of the models attempted have well-chosen hyperparameters. There's a lot of reasons. | |
| Mar 25, 2022 at 18:09 | answer | added | Underminer | timeline score: 2 | |
| Mar 25, 2022 at 17:58 | history | edited | Underminer |
svm tag added
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| Mar 25, 2022 at 17:44 | comment | added | floyd | @Dave yes. I have 458197 data points. but I took 90% of the data for training | |
| Mar 25, 2022 at 17:41 | comment | added | Dave | Do you have many observations? | |
| Mar 25, 2022 at 17:28 | history | asked | floyd | CC BY-SA 4.0 |