Classification Threshold

Classification threshold is a value that separates the positive and negative classes in a binary classification problem. It is used to determine the probability threshold above which an observation is classified as positive and below which it is classified as negative. The classification threshold is an important parameter in machine learning models as it affects the model's precision, recall, accuracy, and F1 score. A low threshold will result in a high recall but low precision, while a high threshold will result in a high precision but low recall. The choice of threshold depends on the specific problem and the trade-off between precision and recall that is acceptable. In some cases, the threshold can be adjusted to achieve a desired balance between precision and recall.
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