Yes, you can use a different number. It will produce a different outcome when compared to 42, which can be used to evaluate your experiment in distinct scenarios.

Also, you are probably using '42' because of this (from wikipedia): The number 42 is, in The Hitchhiker's Guide to the Galaxy by Douglas Adams, the "Answer to the Ultimate Question of Life, the Universe, and Everything" =)

Hi friend, the number 42 just corresponds to the seed of Randomness, you can use another number, for example 101. Each time that you run your model, the values selected will be the same, but if you change this number the values selected will be different and your accuracy could have variety but you will be sure that your model is robust if your accuracy keeps following the same. Best regards.

Yes you can arbitrarily use any appropriate number. Accuracy or performance of proposed method/algorithm/techniques/functions/models varies for the nature or types of databases as well as for the different tasks.

Thanks Mário , José and Muhammad

what is the optimal integer can be used it?

Hi @Mohammed y kamil,

The act of setting the random_state parameter is more important than what number we set it to!

I agree that there might be some performance gain/loss when changing the random_state, but that's where Cross-validation comes in to provide a much better measure of the metric you are using for your dataset.

It's not worthwhile trying to find the best possible integer for your random_state parameter whether you are using cross-validation or not

42 is just a random number that helps to reproduce the same result after reuse the train_test_split. If we want, we can choose other numbers as well.

yes, you can use other numbers instead of 42. It will be useful if you want to create different random numbers for each run, so instead of a specific number, you can put a random number there and enjoy it!

you can set up any int number for random_state

There's no optimal value for random seed, different pseudo random states may and most likely will (depends on the problem) produce different results. You may try a few and use the one which is suitable for your problem. A simple trick is trying ode and even numbers.

Thanks Ashish,

I agree. Cross-validation provides the best measure for a data set.

Regards