Timeline for How to improve the accuracy of my MLP (Current benchmark 77%)
Current License: CC BY-SA 4.0
13 events
| when toggle format | what | by | license | comment | |
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| Sep 5, 2019 at 13:30 | comment | added | Sheetal Patel | that would be very useful thanks. | |
| Sep 5, 2019 at 13:29 | comment | added | Nikos H. | You did not come across as rude :) don't worry. I am just asking so I could check the source myself, since this does not follow state of the art literature. Neural networks can be seen as universal approximators but they are not always the optimal solution to our problems. I am going to write a detailed answer on what you could try later in the day :) | |
| Sep 5, 2019 at 13:15 | comment | added | Sheetal Patel | im sorry if i sounded rude. I am a self learning noobie in the world of data science. ive seen many youtube tutorials and thus i cam to a conclusion that nenural networks generally work well when compared to the other approaches. for the matter of fact. NN giving such poor performance just swept me off a little. i know im going wrong somewhere. its just that i learn from these type of silly mistakes. | |
| Sep 5, 2019 at 13:06 | comment | added | Nikos H. | Could you elaborate on the "heard/seen" part? There are many domains where NN are not performing better-or in order to perform better they require great complexity and computational power. NNs are not panacea for all problems. Since you have metrics that you consider good for your problem I would propose to move on. If you want to even further enhance your results or make more robust predictions try model ensembles. What I going for is that NNs might just not work that well here. Now for the sake of argument you can try a bigger network, training for longer and random search for HPO. | |
| Sep 5, 2019 at 12:54 | comment | added | Sheetal Patel | hi i have already implemented other machine learning models like random forests, Gradient boosting and Näive Bayes where i got an accuracy of 95%, 93% and 78% respectively. the reason why im so particular about NN is that. im experimenting stuff ie: trying different models. so i just want to know where i am going wrong because what i've seen/heard is that Neural networks should actually work better comparatively. not better than the rest but atleast a very good performance. | |
| Sep 5, 2019 at 12:33 | comment | added | Nikos H. | Before trying any HPO method, try and understand the problem you are solving and evaluate whether the data can provide an adequate representation for this issue. Also, if possible set an optimal bayes error for this task. Moreover try other methods of machine learning before using neural networks and check if you can reach a better result. After that we can re-evaluate the NN further | |
| Sep 5, 2019 at 11:55 | answer | added | RonsenbergVI | timeline score: 0 | |
| Sep 5, 2019 at 11:12 | answer | added | Lana | timeline score: 1 | |
| Sep 5, 2019 at 8:35 | history | edited | Sheetal Patel | CC BY-SA 4.0 |
added 1 character in body
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| S Sep 5, 2019 at 8:33 | history | suggested | Nikos H. | CC BY-SA 4.0 |
Corrected spelling, fixed punctuation, altered title to a more concise one.
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| Sep 5, 2019 at 8:25 | review | Suggested edits | |||
| S Sep 5, 2019 at 8:33 | |||||
| Sep 5, 2019 at 7:35 | review | First posts | |||
| Sep 5, 2019 at 7:57 | |||||
| Sep 5, 2019 at 7:31 | history | asked | Sheetal Patel | CC BY-SA 4.0 |