Binary cross-entropy loss - Revision history

KevinYager at 20:54, 3 February 2023

2023-02-03T20:54:00Z

KevinYager at 20:37, 3 February 2023

2023-02-03T20:37:48Z

KevinYager: Created page with "The binary cross-entropy loss is given by: : $L = - \frac{1}{m} \sum_{i=1}^{m} \left[ y_i \cdot \log{ (\hat{y_i}) } + (1-y_i) \cdot \log{ (1-\hat{y_i}) } \right]$ ..."

2023-02-03T20:36:44Z

Created page with "The binary cross-entropy loss is given by: :<math> L = - \frac{1}{m} \sum_{i=1}^{m} \left[ y_i \cdot \log{ (\hat{y_i}) } + (1-y_i) \cdot \log{ (1-\hat{y_i}) } \right] </math>..."

New page

The binary cross-entropy loss is given by:
:<math>
L = - \frac{1}{m} \sum_{i=1}^{m} \left[ y_i \cdot \log{ (\hat{y_i}) } + (1-y_i) \cdot \log{ (1-\hat{y_i}) } \right]
</math>
for <math>m</math> training examples (indexed by <math>i</math>) where <math>y_i</math> is the class label (0 or 1) and <math>\hat{y_i}</math> is the prediction for that example (i.e. the predicted probability that it is a positive example). Thus <math>1-\hat{y_i}</math> is the probability that it is a negative example.

@@ Line 3: / Line 3: @@
 L = - \frac{1}{m} \sum_{i=1}^{m} \left[ y_i \cdot \log{ (\hat{y_i}) } + (1-y_i) \cdot  \log{ (1-\hat{y_i}) } \right]
 </math>
-for <math>m</math> training examples (indexed by <math>i</math>) where <math>y_i</math> is the class label (0 or 1) and <math>\hat{y_i}</math> is the prediction for that example (i.e. the predicted probability that it is a positive example). Thus <math>1-\hat{y_i}</math> is the probability that it is a negative example. Note that we are adding
+for <math>m</math> training examples (indexed by <math>i</math>) where <math>y_i</math> is the class label (0 or 1) and <math>\hat{y_i}</math> is the prediction for that example (i.e. the predicted probability that it is a positive example). Thus <math>1-\hat{y_i}</math> is the probability that it is a negative example.

Binary cross-entropy loss - Revision history

KevinYager at 20:54, 3 February 2023

KevinYager at 20:37, 3 February 2023

KevinYager: Created page with "The binary cross-entropy loss is given by: : L = - \frac{1}{m} \sum_{i=1}^{m} \left[ y_i \cdot \log{ (\hat{y_i}) } + (1-y_i) \cdot \log{ (1-\hat{y_i}) } \right] ..."

KevinYager: Created page with "The binary cross-entropy loss is given by: : $L = - \frac{1}{m} \sum_{i=1}^{m} \left[ y_i \cdot \log{ (\hat{y_i}) } + (1-y_i) \cdot \log{ (1-\hat{y_i}) } \right]$ ..."