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When do you need to change the default threshold?
As such, there is often the need to change the default decision threshold when interpreting the predictions of a model. … almost all classifiers generate positive or negative predictions by applying a threshold to a score. The choice of this threshold will have an impact in the trade-offs of positive and negative errors.
When to use threshold moving for imbalanced data?
The bottom line is that when studying problems with imbalanced data, using the classifiers produced by standard machine learning algorithms without adjusting the output threshold may well be a critical mistake. — Machine Learning from Imbalanced Data Sets 101, 2000.
What should the threshold be for not spam?
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.
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.
When to use a threshold in logistic regression?
In order to map a logistic regression value to a binary category, you must define a classification threshold (also called the decision threshold ). A value above that threshold indicates “spam”; a value below indicates “not spam.”
Which is the correct way to calculate the optimal threshold?
In some cases, the optimal threshold can be calculated directly. Tuning or shifting the decision threshold in order to accommodate the broader requirements of the classification problem is generally referred to as “ threshold-moving ,” “ threshold-tuning ,” or simply “ thresholding .”
What happens when you change the classification threshold?
The following sections take a closer look at metrics you can use to evaluate a classification model’s predictions, as well as the impact of changing the classification threshold on these predictions. Note: “Tuning” a threshold for logistic regression is different from tuning hyperparameters such as learning rate.