In the realm of machine learning, a Type 1 error, otherwise referred to as a false positive (FP), transpires when a model erroneously forecasts the existence of a particular condition or attribute that is, in fact, absent. For instance, a model might misclassify a legitimate email as spam.
Type 1 errors pose a substantial issue in machine - learning applications. The implications of false positives can be not only costly but also detrimental. In the field of medical diagnosis, for example, a false positive outcome could trigger needless medical procedures or treatments.
To mitigate the risk of Type 1 errors in machine learning, numerous techniques can be utilized. One viable approach is to fine-tune the model's decision threshold, rendering its predictions more conservative. This can be accomplished by raising the threshold for a positive prediction. Although this may lead to a reduction in false positives, it might simultaneously increase the number of false negatives.