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A seed usually enables you to reproduce the sequence of random numbers. In that sense they are not true random numbers but "pseudo random numbers", hence a PNR Generator (PNRG). These are a real help in real life! A bit more detail: Virtually all "random" number generators implemented in computer languages are pseudo random number generators.
The set.seed ()function in R takes an (arbitrary) integer argument. So we can take any argument, say, 1 or 123 or 300 or 12345 to get the reproducible random numbers. Also, in theTeachingDemos package, the char2seed function allows user to set the seed based on a character string. Highly active question.
The use of a seed is essential if one wants to ensure the reproducibility of results. For example, the R random number generator function set.seed() would allow you to restart the sequence by simply resetting the seed to the same value that was used previously. For example. set.seed(5) X <- rnorm(10) design1 <- sample(X) design2 <- sample(X)
Snippet of code below: # If a random number generator seed exists. if self.random_generator_seed: # Create a new random number generator for this instance based on its. # own index. self.random_generator_seed += instance_index. self.random_number_generator = RandomState(self.random_generator_seed) Essentially I start off with a user-inputted ...
1 Answer. For pseudo-random numbers, the seed is not there to "ensure randomness". In fact, it is quite the opposite: the seed is there to ensure reproducibility. You set the seed if you want to be able to run the same pseudo-random Monte Carlo experiments again and get the exact same results.
Here's what I think: setting the seed will give consistent results (assume model is trained and deployed on same device, as device can also affect randomness) if i need to set the seed to get good results, that means my model does not generalize well (hence, i should retrain model until seed does not have a significant effect on performance)
If you seed it directly, you need to provide 624 32-bit numbers as a seed. Since it's usually undesirable to have to do that, by default you give a wrapper function a 32-bit (or possibly 64-bit) seed, which passes it to a simpler, lower-quality pseudorandom number generator. This is used to generate the 624x32-bit seed for the Mersenne Twister.
That is, if we used first 10 rows on tree_1, in random forest_1 then we will use the same rows in tree_1 of random_forest_2, or random_forest_3 etc. Setting seed for random number generator makes the results reproducible. So if you use, and record the seed, then when running the algorithm next time with the same data and same hyperparameters ...
The same help page explains that if you haven't used random number generation before or set the seed, to begin with the seed is taken from the clock, and thereafter the previous value is stored (so that the next random number you get would be the same one you'd have obtained if you'd generated one more value last time -- it remembers "where you ...
random generation - Set seed before each code block or once per project? - Cross Validated. Set seed before each code block or once per project? It is standard advice to set a random seed so that results can be reproduced. However, since the seed is advanced as pseudo-random numbers are drawn, the results could change if any piece of code draws ...