Row 12109

Row ID: 12109 | Dataset Entry | Axioma AXP Content Repository

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I work in engineering and use xgboost and lgbm when dealing with dozens of sensors for classification problems when the sensors are direct, cuz its just a lot if conditions. They are preferable to NN as they are lightweight and don't overfit like NN. In other, more vague problems or when the sensors aren't direct, I tend to use more complex algorithms, usually NN.

I know that your problem is different, but I'd honestly recommend giving it a try. It's 2 or 3 lines of code that you just copy from sklearn library. It's not that much effort and should give you a baseline on what to least expect from your model.

FieldValue
text I work in engineering and use xgboost and lgbm when dealing with dozens of sensors for classification problems when the sensors are direct, cuz its just a lot if conditions. They are preferable to NN as they are lightweight and don't overfit like NN. In other, more vague problems or when the sensors aren't direct, I tend to use more complex algorithms, usually NN.  I know that your problem is different, but I'd honestly recommend giving it a try. It's 2 or 3 lines of code that you just copy fr…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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{
  "text": "I work in engineering and use xgboost and lgbm when dealing with dozens of sensors for classification problems when the sensors are direct, cuz its just a lot if conditions. They are preferable to NN as they are lightweight and don't overfit like NN. In other, more vague problems or when the sensors aren't direct, I tend to use more complex algorithms, usually NN. \n\n\nI know that your problem is different, but I'd honestly recommend giving it a try. It's 2 or 3 lines of code that you just copy from sklearn library. It's not that much effort and should give you a baseline on what to least expect from your model.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
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}
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