Effect of Threshold Selection
Let’s see how threshold influences predictions.
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Influence of threshold
When we perform binary classification by default, we assume the threshold to be 0.5. If the prediction score is greater than it, we consider the prediction positive. However, we can manipulate the threshold value to modify model behavior.
For example, if we are very concerned about false positives, we can increase the threshold to make the model more conservative. The prediction will be positive only when the score is high, and the model is very confident about it. Threshold selection is the ...