Row 60700

Row ID: 60700 | Dataset Entry | Axioma AXP Content Repository

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Well the one I found seemed to lay out the problem with ohe very well (dimensionality explosion with very spare data) and proposed an embedded layer as a solution, so ideally I'd like that one but I realize that might be like finding a grain of salt in a pile of sand.

So, any papers that specifically discuss the pros and cons of these two methods would suffice. I've found several that discuss embedding techniques, but I liked the idea of an embedding layer because (if I'm not mistaken), the authors created the layer size with len(df[col].nunique()). If I'm remembering that right and it works, I think it would be a great way to create a model that is flexible enough to help with the training of a large population model but specific enough for deploying to a single user, because the size of the layer is created dynamically each time based on the number of distinct values in an "identifier" column.

I'm trying to convince my peers that one hot encoding is not the best way to transform it identifier variable, because the dataset will become so sparse that the variable will essentially become meaningless within the calculations, and want to back up my stance.

FieldValue
text Well the one I found seemed to lay out the problem with ohe very well (dimensionality explosion with very spare data) and proposed an embedded layer as a solution, so ideally I'd like that one but I realize that might be like finding a grain of salt in a pile of sand. So, any papers that specifically discuss the pros and cons of these two methods would suffice. I've found several that discuss embedding techniques, but I liked the idea of an embedding layer because (if I'm not mistaken), the aut…
label r/datascience
dataType comment
communityName r/datascience
datetime 2024-05-23
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url_encoded Z0FBQUFBQm5Lak9wb1lrWkt2WkZDMnlZTUdVWGdjb3ZOQU14NzFrcVBWbEVnWjBlRlNVUGc4R2FNWmlON1dCaXNrc214SXBjNmhCZ21ibUFyZDNocmhMLVV1OV8taVRMcktJOExUeldla0lWUUllMFV1WElPb0plbVZKcHhXQTVseFViaTgyT25OckUxemRPNmpsc2lMQTlWRFVBaFFyWmZXSXZqMDJZd3B6UW9adXdiQTJ2SXRFREZ4WE9PckhwWjdIeUJDbXNUdDNE

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{
  "text": "Well the one I found seemed to lay out the problem with ohe very well (dimensionality explosion with very spare data) and proposed an embedded layer as a solution, so ideally I'd like that one but I realize that might be like finding a grain of salt in a pile of sand.\n\nSo, any papers that specifically discuss the pros and cons of these two methods would suffice. I've found several that discuss embedding techniques, but I liked the idea of an embedding layer because (if I'm not mistaken), the authors created the layer size with len(df[col].nunique()). If I'm remembering that right and it works, I think it would be a great way to create a model that is flexible enough to help with the training of a large population model but specific enough for deploying to a single user, because the size of the layer is created dynamically each time based on the number of distinct values in an \"identifier\" column.\n\nI'm trying to convince my peers that one hot encoding is not the best way to transform it identifier variable, because the dataset will become so sparse that the variable will essentially become meaningless within the calculations, and want to back up my stance.",
  "label": "r/datascience",
  "dataType": "comment",
  "communityName": "r/datascience",
  "datetime": "2024-05-23",
  "username_encoded": "Z0FBQUFBQm5Lak1aTFlHeTVDVUI0ejJ4X0FIX0lVelVLMDFHWU9PWDZjalpSNFNDN210WG51dzV6SDJJbUEtLUYzNldESXA1MkdMQ2w1OVZYeFpxdE82amRMaEhWdW9CWnc9PQ==",
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}
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