Row 20995

Row ID: 20995 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 20995 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I am aware of several algorithms from \`scikit-learn\` such as LR, SVR, KNR etc.

The thing is they are quite limited

* Accept only one dimensional input (whereas my data is multi-dimensional, flattening to 1D didn't show better performance compared to a 2D CNN) * Difficult to work with large amount of data * Not many work with multi-output regression, or they'd require some tweaks (e.g., one \`regressor\` for one output)

Please correct me if I'm wrong, I know that there is \`partial\_fit\` but I couldn't find many discussions about the effectiveness of this approach. Regression problem seems to be a lot less popular than classification in terms of available open-source projects.

Regarding neural networks, one also may argue that we can just modify the \`head\` to perform regression, is that really that simple?

In summary, I would like to know if anyone working with regression tasks, do you

* Only use classic ML algorithms. In that case, how did you overcome the limitations mentioned above. The data I am working with is audio wav form and it can have a size of 1000 x 5000 or so. * Or have you used neural networks to perform multi-output regression? Which architectures would you recommend . Any details we need to be aware of?

Thank you.

FieldValue
text I am aware of several algorithms from \`scikit-learn\` such as LR, SVR, KNR etc. The thing is they are quite limited * Accept only one dimensional input (whereas my data is multi-dimensional, flattening to 1D didn't show better performance compared to a 2D CNN) * Difficult to work with large amount of data * Not many work with multi-output regression, or they'd require some tweaks (e.g., one \`regressor\` for one output) Please correct me if I'm wrong, I know that there is \`partial\_fit\` bu…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-21
username_encoded Z0FBQUFBQm5Lak1BM056SENfdXNOdktOYXNiMDhzSnczemRZeDllTkRJS3MwWG40TFA3VHdqMHgwNEJ2Z1AydWRBWm1ZYTRmTTVLNXdwZGtsUWtFanlJNFlnQWd6SXVNVnc9PQ==
url_encoded Z0FBQUFBQm5Lak9QRkUteXhxU29NaHdsSjdqZnFCV2lvcWNUZVdCcGRlNlVlYVo5NU1RM01Ub3FhRkRWakJtS0NSZ1labzllV2xHek0xSjlYT1F2M0NTS3EwZFpSSXVrakQzSmtzLUVYLXR6eVBKNjYwYS1kb2FZclEyLU96a3g2UjhTcGhpTkhrSHc3MUMwOGp5VzlLaTRpNnZ1YkJ6QkczMi1LY1diaUQ0d1FrbzB6TVJYX0tGS19Sd0RhemloT3FxbG5qRG5NbnRP

Raw Record

{
  "text": "I am aware of several algorithms from \\`scikit-learn\\` such as LR, SVR, KNR etc.\n\nThe thing is they are quite limited\n\n* Accept only one dimensional input (whereas my data is multi-dimensional, flattening to 1D didn't show better performance compared to a 2D CNN)\n* Difficult to work with large amount of data\n* Not many work with multi-output regression, or they'd require some tweaks (e.g., one \\`regressor\\` for one output)\n\nPlease correct me if I'm wrong, I know that there is \\`partial\\_fit\\` but I couldn't find many discussions about the effectiveness of this approach. Regression problem seems to be a lot less popular than classification in terms of available open-source projects.\n\nRegarding neural networks, one also may argue that we can just modify the \\`head\\` to perform regression, is that really that simple?\n\nIn summary, I would like to know if anyone working with regression tasks, do you\n\n* Only use classic ML algorithms. In that case, how did you overcome the limitations mentioned above. The data I am working with is audio wav form and it can have a size of 1000 x 5000 or so.\n* Or have you used neural networks to perform multi-output regression? Which architectures would you recommend . Any details we need to be aware of?\n\nThank you.",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1BM056SENfdXNOdktOYXNiMDhzSnczemRZeDllTkRJS3MwWG40TFA3VHdqMHgwNEJ2Z1AydWRBWm1ZYTRmTTVLNXdwZGtsUWtFanlJNFlnQWd6SXVNVnc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9QRkUteXhxU29NaHdsSjdqZnFCV2lvcWNUZVdCcGRlNlVlYVo5NU1RM01Ub3FhRkRWakJtS0NSZ1labzllV2xHek0xSjlYT1F2M0NTS3EwZFpSSXVrakQzSmtzLUVYLXR6eVBKNjYwYS1kb2FZclEyLU96a3g2UjhTcGhpTkhrSHc3MUMwOGp5VzlLaTRpNnZ1YkJ6QkczMi1LY1diaUQ0d1FrbzB6TVJYX0tGS19Sd0RhemloT3FxbG5qRG5NbnRP"
}
Explore Dataset Explore Row

Entry Information