Random Means

Random Means As noted numpy random seed 0 sets the random seed to 0 so the pseudo random numbers you get from random will start from the same point This can be good for debuging in some cases HOWEVER after some reading this seems to be the wrong way to go at it if you have threads because it is not thread safe

Random state ensures that the splits that you generate are reproducible Scikit learn uses random permutations to generate the splits The random state that you provide is used as a seed to the random number generator This ensures that the random numbers are generated in the same order For all random datasets each is assigned a random state value This means that one random state value has a fixed dataset so every time we run the code with random state value 1 it will produce the same splitting datasets The image below shows everything that random state does See also What is random state

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Random Means
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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 points as centroid if you use random initialization This helps when one wants to reproduce results at some later point in time When you from random import all the definitions from random become part of the current name space This means you don t have to prefix anything with random but it also means you may get a name collision without even knowing it The preferred way is import random

The numpy random library contains a few extra probability distributions commonly used in scientific research as well as a couple of convenience functions for generating arrays of random data The random random library is a little more lightweight and should be fine if you re not doing scientific research or other kinds of work in statistics Random state is None by default which means every time when you run your program you will get different output because of splitting between train and test varies within random state any int value means every time when you run your program you will get tehe same output because of splitting between train and test does not varies within

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Rand state np random RandomState 1 k means KMeans n clusters k init k means random state rand state n jobs 1 but this also does not give consistent results with the same function with just one job Also as I do have to do some quite heavy clustering on lots of data I d really like to use multiple jobs to save time I wanted a longish random string with a short as possible routine for a code demo that doesn t need to be cryptographically amazing just produce some nice random visual fluff I like this answer the best your 2nd one so thank you

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What Does Numpy random seed 0 Do Stack Overflow

https://stackoverflow.com › questions
As noted numpy random seed 0 sets the random seed to 0 so the pseudo random numbers you get from random will start from the same point This can be good for debuging in some cases HOWEVER after some reading this seems to be the wrong way to go at it if you have threads because it is not thread safe

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Too Many Focus On The First Act Please Understand This Quote Above

What Is quot random state quot In Sklearn model selection train test split

https://stackoverflow.com › questions
Random state ensures that the splits that you generate are reproducible Scikit learn uses random permutations to generate the splits The random state that you provide is used as a seed to the random number generator This ensures that the random numbers are generated in the same order

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