Row 3711

Row ID: 3711 | Dataset Entry | Axioma AXP Content Repository

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Accuracy can be very deceiving if your dataset is imbalanced. Imagine if you have a validation set (are you splitting training and validation?) that has 95 samples of class 0 and 5 of class 1. A model that just calls everything class 0 would get 95 accuracy. How is the F1 score? Then about it seemingly depending on the initialization, well initializing the weights is one thing that you could look into. Also maybe a lower learning rate to make sure it's not overshooting ?

FieldValue
text Accuracy can be very deceiving if your dataset is imbalanced. Imagine if you have a validation set (are you splitting training and validation?) that has 95 samples of class 0 and 5 of class 1. A model that just calls everything class 0 would get 95 accuracy. How is the F1 score? Then about it seemingly depending on the initialization, well initializing the weights is one thing that you could look into. Also maybe a lower learning rate to make sure it's not …
label r/neuralnetworks
dataType comment
communityName r/neuralnetworks
datetime 2024-03-21
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{
  "text": "Accuracy can be very deceiving if your dataset is imbalanced. Imagine if you have a validation set (are you splitting training and validation?) that has 95 samples of class 0 and 5 of class 1. A model that just calls everything class 0 would get 95 accuracy.          \n          \nHow is the F1 score?         \n        \nThen about it seemingly depending on the initialization, well initializing the weights is one thing that you could look into. Also maybe a lower learning rate to make sure it's not overshooting ?",
  "label": "r/neuralnetworks",
  "dataType": "comment",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-03-21",
  "username_encoded": "Z0FBQUFBQm5LakwxZmQyZzlESTlOQ2g2aWtybXdDZkVGUUdDR1RGV2FKa3dSSFp3bHJzNndUbkIxMUdHMGtQSTc0NDBtdDB3bzhVb3ZReE9IaU5vMjB2MndIbllNR3NNbFE9PQ==",
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}
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