It measures how well the predicted probabilities align with the true class labels, making it ideal for problems like multi-class classification. Lower cross-entropy indicates better model performance. Cricbet99 create account

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Cross-entropy loss is calculated by taking the difference between our prediction and actual output. We then multiply that value with -y * ln(y). This means we take a negative number, raise it to the power of the logarithm of y (which will be positive), and then subtract this from our original calculation. Credit essay writing service cheap