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To do the binary classification of a set of images, I trained the random forest on a set of data. I now want to evaluate the error probability of my model. For that, I did two things and I don't know what corresponds to this error probability:

  1. I calculated the accuracy using the k-fold cross validation
  2. I tested my model after I calculated the ratio between the misclassified images and the total number of images.

What is the correct way to calculate the probability of error for my trained model?

Julia Meshcheryakova
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Sab
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  • Welcome to SO! It's a question of business logic which metric you rely on. 'Probability of error' might be accuracy, balanced accuracy, precision, recall, f1-score. Could you, please, be more specific? Please, also refer to https://stackoverflow.com/questions/46598301/how-to-compute-precision-recall-and-f1-score-of-an-imbalanced-dataset-for-k-fold – Julia Meshcheryakova Dec 28 '22 at 19:34

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