Row 23013

Row ID: 23013 | Dataset Entry | Axioma AXP Content Repository

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Honestly this is not the feeling I got from your question.

Some of the models you mentioned in sklearn can perform multiple regression.

The dimensionality you mentioned is not at all difficult to handle

I don't get what you mean with accept single inputs

No, regression is not less frequent than classification.

Partial fit is not a method or an approach, it's just one step of gradient descent

In general, random forests and gradient boosting machines tend to perform best, besides NN, especially in tabular data, and both of them can do multiple regression.

For your specific case, you mentioned only the dimensionality, not the task or the kind of features, so not much to suggest there.g

FieldValue
text Honestly this is not the feeling I got from your question. Some of the models you mentioned in sklearn can perform multiple regression. The dimensionality you mentioned is not at all difficult to handle I don't get what you mean with accept single inputs No, regression is not less frequent than classification. Partial fit is not a method or an approach, it's just one step of gradient descent In general, random forests and gradient boosting machines tend to perform best, besides NN, esp…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-21
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url_encoded Z0FBQUFBQm5Lak9RRmpMSXhCLWdvTFNlb2x4cVA0RHdXRjdxQmJQNDFydEQtOFFGMUZvYWtreHVfQWMyTmNxWTNvc3MzYktldi01bXJiOFJtZ3RjeTh1em02b3NRcVRQRFAzOGtPcXV1dW9RN3R0UFl2Wjl4STNzbk9UNVR0NHFxZmRnOUQ4NWczcU1NVGdPQXZWZkhDY3V6bl9nTkNDMHM4aFlQS2llSWh6WFl2bml3Z2xOQTJKdmZwOHlYTnA2WWtpd3VlZDZnSVZ6S0dEaFNvQmpnYkRPazlXb2NqUGxUUT09

Raw Record

{
  "text": "Honestly this is not the feeling I got from your question. \n\nSome of the models you mentioned in sklearn can perform multiple regression. \n\nThe dimensionality you mentioned is not at all difficult to handle\n\nI don't get what you mean with accept single inputs \n\nNo, regression is not less frequent than classification.\n\nPartial fit is not a method or an approach, it's just one step of gradient descent \n\nIn general, random forests and gradient boosting machines tend to perform best, besides NN, especially in tabular data, and both of them can do multiple regression. \n\n\nFor your specific case, you mentioned only the dimensionality, not the task or the kind of features, so not much to suggest there.g",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-21",
  "username_encoded": "Z0FBQUFBQm5Lak1COVA2WWU2M3RpZmpoN2tPMVRwckhrSEVWMXFCNUloMEJNX1FXN1htazZLNi1ZQTd6bzVXeGw0WEVRbFp1TGF6S0ltTnJOWVY0cThRMTBDVF9vbnJoZEJZem03RUt2cGRlQTZfQkZqNlltS0E9",
  "url_encoded": "Z0FBQUFBQm5Lak9RRmpMSXhCLWdvTFNlb2x4cVA0RHdXRjdxQmJQNDFydEQtOFFGMUZvYWtreHVfQWMyTmNxWTNvc3MzYktldi01bXJiOFJtZ3RjeTh1em02b3NRcVRQRFAzOGtPcXV1dW9RN3R0UFl2Wjl4STNzbk9UNVR0NHFxZmRnOUQ4NWczcU1NVGdPQXZWZkhDY3V6bl9nTkNDMHM4aFlQS2llSWh6WFl2bml3Z2xOQTJKdmZwOHlYTnA2WWtpd3VlZDZnSVZ6S0dEaFNvQmpnYkRPazlXb2NqUGxUUT09"
}
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