Row 23102

Row ID: 23102 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 23102 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

I'll skip the first half of your answer since we both know how to handle such things, and in fact I've implemented and tested with all mentioned previously models.

>> accept single inputs

I did not say that. I said one "dimensional input" (i.e., the row in tabular data).

To be it's not that good when flattening the input features to 1D. I've tested with different CNN architectures and they can easily outperform the multi-output regressors from \`scikit-learn\`.

As I mentioned I work with "audio" (raw audio, which can be \`FFT\`-transformed into different spectral features and cepstral coefficient. They are not tabular data.

>> No, regression is not less frequent than classification.

If you have a look at ANY model repository (e.g., huggingface), classification tasks are everywhere. You can prove me wrong.

If you're not clear about the question I think it's better to give some suggestions (like you just did) instead of throwing the thread to another sub.

FieldValue
text I'll skip the first half of your answer since we both know how to handle such things, and in fact I've implemented and tested with all mentioned previously models. >> accept single inputs I did not say that. I said one "dimensional input" (i.e., the row in tabular data). To be it's not that good when flattening the input features to 1D. I've tested with different CNN architectures and they can easily outperform the multi-output regressors from \`scikit-learn\`. As I mentioned I work with "au…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-21
username_encoded Z0FBQUFBQm5Lak1CYTNFYmVkLXBUZ2VvTlNJSnY2TEticEdzeXJGeXByR3JiRllNY0xfM2lpTlFOengzNWJpYTdJdFNTSG9nQ1RjOGRUT0xQVjh4b0lEVzFiSDdFcjUzVVE9PQ==
url_encoded Z0FBQUFBQm5Lak9RdE9RTWhSOWhDalNhZ2tVaUt2NktqWVVCb25vSTBQQ3NQQzM0S1kyR3NQQ3R6UjVVSWFVaUpBQkpDenRCTzdCLVF0Qm5wQmFWZDdqdjFxZzBsLWU3RUpPLUJmLXpkamJpLUN6R0tPejVpYWtoajJDN3FqTkxadGZCSG1KU1JWX1lJeDdka1BucnQ2NHh1MlY1VU9zTGhtUmpHMUxITnJpSmpZcjlaVlZLUE1QWXA1bjVGVnIzNHYwMXVwYW9MTUlEell0czdwY0NYMFFuY2E5T0U2a3lWdz09

Raw Record

{
  "text": "I'll skip the first half of your answer since we both know how to handle such things, and in fact I've implemented and tested with all mentioned previously models.\n\n>> accept single inputs\n\nI did not say that. I said one \"dimensional input\" (i.e., the row in tabular data).\n\nTo be it's not that good when flattening the input features to 1D. I've tested with different CNN architectures and they can easily outperform the multi-output regressors from \\`scikit-learn\\`.\n\nAs I mentioned I work with \"audio\" (raw audio, which can be \\`FFT\\`-transformed into different spectral features and cepstral coefficient. They are not tabular data.\n\n>> No, regression is not less frequent than classification.\n\nIf you have a look at ANY model repository (e.g., huggingface), classification tasks are everywhere. You can prove me wrong.\n\nIf you're not clear about the question I think it's better to give some suggestions (like you just did) instead of throwing the thread to another sub.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1CYTNFYmVkLXBUZ2VvTlNJSnY2TEticEdzeXJGeXByR3JiRllNY0xfM2lpTlFOengzNWJpYTdJdFNTSG9nQ1RjOGRUT0xQVjh4b0lEVzFiSDdFcjUzVVE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9RdE9RTWhSOWhDalNhZ2tVaUt2NktqWVVCb25vSTBQQ3NQQzM0S1kyR3NQQ3R6UjVVSWFVaUpBQkpDenRCTzdCLVF0Qm5wQmFWZDdqdjFxZzBsLWU3RUpPLUJmLXpkamJpLUN6R0tPejVpYWtoajJDN3FqTkxadGZCSG1KU1JWX1lJeDdka1BucnQ2NHh1MlY1VU9zTGhtUmpHMUxITnJpSmpZcjlaVlZLUE1QWXA1bjVGVnIzNHYwMXVwYW9MTUlEell0czdwY0NYMFFuY2E5T0U2a3lWdz09"
}
Explore Dataset Explore Row

Entry Information