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So, the results shown in Fig. 2.13 show that the real data sample is even smaller than I expected. Of course, both results turned out to be noticeably worse compared to my forecast, but this is not scary. It’s just that the reset table indicates that these values â€‹â€‹\\u200b\\u200bare obtained on the basis of average data, that is, not on our own, but on the more average of many other results.
I understand that the final results are rather strange, because at first I assumed that we correctly chose those parameters for generating predictors that are associated with known properties, and then changed these parameters. Ultimately, if we used only the characteristics known to us, it would be even worse, because the data could simply not match, although there is a search for such cases.
â€” While writing this book, I wrote several books in which I argued that it is enough to study those properties that are associated with certain predictors, for example, a predictor of smoking habit. Next, I replaced the predictors known to me with other known ones, which, as I believed, were associated with other predictors. As a result, my predictors in this book matched completely unknown predictors. However, I knew perfectly well that in this case all my predictions would be wrong.
â€œSo you have to change the predictors without changing the dataâ€ is the point of our discussion today. To add to this, I think I still do not have the opportunity to change the data, since I do not know where in the living system the factors that I will use are located.
Predictions can only be changed after you know them. Then, even