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?Continue…
Best links of the week from 20th May to 26th May
- UN (United Nations) data.
- A curated list of 200+ blogs related to Data Science at CybrHome.
- 25 Excellent Machine Learning Open Datasets.
- Group Chats Are Making the Internet Fun Again at Intelligencer.
- Do anything with dplyr.
- Starting out with R at Credibly Curious.
Como o atual presidente do Brasil se compara em termos de número de decretos com seus predecessores?Continue…