How can I evaluate my model? Part I.

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One way to evaluate your model is in terms of error types. Let’s consider a scenario where you live in a city where it rains every once in a while. If you guessed that it would rain this morning, but it did not, your guess was a false positive, sometimes abbreviated as FP. If you said it would not rain, but it did, then you had a false negative (FN). Raining when you do not have an umbrella may be annoying, but life is not always that bad. You could have predicted that it would rain and it did (true positive, TP) or predicted that it would not rain and it did not (true negative, TN). In this example, it’s easy to see that in some contexts one error may be worse than the other and this will vary according to the problem. Bringing an umbrella with you in a day with no rain is not as bad as not bringing an umbrella on a rainy day, right?

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