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What are classification thresholds?
A value above that threshold indicates “spam”; a value below indicates “not spam.” It is tempting to assume that the classification threshold should always be 0.5, but thresholds are problem-dependent, and are therefore values that you must tune. …
Why is ROC curve used?
ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. In addition, the area under the ROC curve gives an idea about the benefit of using the test(s) in question.
Which is an example of threshold moving for binary classification?
For example, on a binary classification problem with class labels 0 and 1, normalized predicted probabilities and a threshold of 0.5, then values less than the threshold of 0.5 are assigned to class 0 and values greater than or equal to 0.5 are assigned to class 1.
Is there a way to reduce the threshold for classification?
However, if this is not correct thinking of reducing the threshold, what would be some data transformations, which emphasize individual features in a similar manner, so that the threshold can remain at 0.5? Frank Harrell has written about this on his blog: Classification vs. Prediction, which I agree with wholeheartedly.
Which is the default threshold for class imbalance?
This is achieved by using a threshold, such as 0.5, where all values equal or greater than the threshold are mapped to one class and all other values are mapped to another class. For those classification problems that have a severe class imbalance, the default threshold can result in poor performance.
How to find the best threshold for a classifier?
In some cases, such as when using ROC Curves and Precision-Recall Curves, the best or optimal threshold for the classifier can be calculated directly. In other cases, it is possible to use a grid search to tune the threshold and locate the optimal value.