What exactly is a seed in a random number generator?
网页2018年7月4日 · The purpose of the seed is to allow the user to "lock" the pseudo-random number generator, to allow replicable analysis. Some analysts like to set the seed using a true random-number generator (TRNG) which uses hardware inputs to generate an initial seed number, and then report this as a locked number. If the seed is set and reported by …
How do I use random seed appropriately? - Cross Validated
网页2018年9月19日 · 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 <- …
random generation - New or same seed for each Monte Carlo …
网页2016年9月16日 · 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. For example if your scripts will be archived with an eventual publication. It does not make sense to set the seed 10,000 times.
Random number-Set.seed(N) in R - Cross Validated
网页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.
Setting random seed for final neural network model
网页2021年7月6日 · Let's say I had to create a supervised learning model for some classification task. To start, I would set a random seed for doing train/test splits, for cross-validation splits on the training set, and for model random states. This way all the results would be reproducible. Once that is done, I would take the best model from the cross ...
cross validation - Seed in a grid search - Cross Validated
网页2018年1月19日 · A very popular technique following the idea you formulate in your question is random search. Once you see it, the idea is quite simple, and it is shown to work well in practice. Consider you search space with a finite maximum. Take the 5% interval around the maximum. If you sample at random, you have 5% chance of landing in that interval.
Why does changing random seeds alter results? - svm
网页2018年11月18日 · In order to be useful, stochastic algorithms are generally insensitive to the random number seed. the long answer. Classical statistical methods (t-test, ANOVA, regression etc.) are deterministic algorithms, but many modern algorithmic approaches include a stochastic component. (In between are methods like k-means clustering or …
Is there such a thing as a "good/bad" seed in pseudo-random …
网页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.
machine learning - Why a lot of people use random.seed() at the ...
网页2020年7月29日 · A lot of people use random.seed() at the beginning of their python code in training a machine learning model. As I understand they want to control the randomness therefore they can compare different hyper-parameters, but as demonstrated in the following piece of python code, the random int number is controlled only when you set seed every …
Why does sklearn.grid_search.GridSearchCV return random results …
网页2017年3月24日 · You can avoid this by using any value for random_state parameter as mentioned in documentation:. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the …
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