How To Fix Type 1 Error Matrix?

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    Confusion matrices store two types of errors: Type I and Type II. False positives (FP) of type I, and therefore type II, became positive, which the model had wrongly hoped for. This is also known as Type I error. In our example, this means that patients who predicted they had cancer were in fact healthy. https://towardsdatascience.com › classifying-model-outcomes-t Assessing the key performance of machine learning models. A false positive is a Type I error because a false positive = false true and has only one specific F. A false negative is a Type II error because a false negative is equivalent to a false false, so always two Fs make it a Type II error.