Jan 29, 2019 · In practice I would say, you should set the random_state to some fixed number while you test stuff, but then remove it in production if you really need a random (and not a fixed) split. …

Aug 24, 2015 · It's therefore impossible to map every RNG state to a unique integer seed. You can get and set the internal state of the RNG directly using np.random.get_state and np.random.set_state. …

Aug 26, 2016 · Confused about random_state parameter, not sure why decision tree training needs some randomness. My thoughts is it related to random forest? is it related to split training testing …

I know that to seed the randomness of numpy.random, and be able to reproduce it, I should us: import numpy as np np.random.seed(1234) but what does np.random.RandomState() do?

Mar 7, 2018 · On a serious note, random_state simply sets a seed to the random generator, so that your train-test splits are always deterministic. If you don't set a seed, it is different each time.

Jan 24, 2020 · 3 Random state in Kmeans function of sklearn mainly helps to Start with same random data point as centroid if you use Kmeans++ for initializing centroids. Start with same K random data …

Oct 22, 2013 · You could keep track of the number of calls made to System.Random, so you'd have a count for when the failure occurred. To restore the state, just set the same original seed, and call the …

Mar 11, 2019 · When calling torch.get_rng_state you will get your random number generator state as a torch.ByteTensor. You can then save this tensor somewhere in a file and later you can load and use …

Feb 21, 2018 · In scikit-learn, Lasso and Ridge regression are two regression methods which have a random_state attribute. Why do these two methods need this attribute? From the documentation: …

Jan 24, 2021 · It is used to create a random number generator with a set seed (123). It is useful because using seed() impacts the global numpy random generation, while RandomState will set the seed for …