Row 7423

Row ID: 7423 | Dataset Entry | Axioma AXP Content Repository

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I was going through the paper [On Calibration of Modern Neural Networks](https://arxiv.org/pdf/1706.04599), and saw that the authors used the following definition for the "fraction of positives" which shows up on the y-axis of the calibration curve.

https://preview.redd.it/pq9eqj16bq0d1.png?width=944&format=png&auto=webp&s=be71a70ff0e6ba77b672ca9b4315c6e7ba3d1011

From my understanding, the above equation is calculating the average accuracy in the bin m.

However, my original understanding about the "fraction of positives" was that it was the proportion of actual positive outcomes within the bin m, which intuitively makes more sense in the context of calibration curves. I have also seen this interpretation of calibration curves.

Can you fill in the hole in my knowledge?

FieldValue
text I was going through the paper [On Calibration of Modern Neural Networks](https://arxiv.org/pdf/1706.04599), and saw that the authors used the following definition for the "fraction of positives" which shows up on the y-axis of the calibration curve. https://preview.redd.it/pq9eqj16bq0d1.png?width=944&format=png&auto=webp&s=be71a70ff0e6ba77b672ca9b4315c6e7ba3d1011 From my understanding, the above equation is calculating the average accuracy in the bin m. However, my original understanding abo…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-16
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{
  "text": "I was going through the paper [On Calibration of Modern Neural Networks](https://arxiv.org/pdf/1706.04599), and saw that the authors used the following definition for the \"fraction of positives\" which shows up on the y-axis of the calibration curve. \n\nhttps://preview.redd.it/pq9eqj16bq0d1.png?width=944&format=png&auto=webp&s=be71a70ff0e6ba77b672ca9b4315c6e7ba3d1011\n\nFrom my understanding, the above equation is calculating the average accuracy in the bin m.\n\nHowever, my original understanding about the \"fraction of positives\" was that it was the proportion of actual positive outcomes within the bin m, which intuitively makes more sense in the context of calibration curves. I have also seen this interpretation of calibration curves.\n\nCan you fill in the hole in my knowledge?  ",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-16",
  "username_encoded": "Z0FBQUFBQm5LakwzeHJVbDZ2TFNaTHlvVzdKeDdPX1VtZXVMcDRrd0VhSmxkUTFDdVpwNVROWW1RSmVWaFBNU2JHb0RKdDVMbnJ3ZWl0LWMwN1Y0ZXVFUmF2YTdfbG9CbVE9PQ==",
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