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Can machine learning algorithms predict random number generators.

key

A= 1 2 3

B= 4 5 6

C= 7 8 9

Example to catch a number sequence

4 8 8

I would select B C C

That would give me 27 number combinations

4 7 7

4 8 7

4 9 7

5 7 7

5 8 7

5 9 7

6 7 7

6 8 7

6 9 7

4 7 8

4 8 8 match

4 9 8

5 7 8

5 8 8

5 9 8

6 7 8

6 8 8

6 9 8

4 7 9

4 8 9

4 9 9

5 7 9

5 8 9

5 9 9

6 7 9

6 8 9

6 9 9

Using the example above can a sequence prediction algorithm predict the next sequence

Data

B C C

C C A

A C A

C A A

C B B

C C A

B C C

A C B

C B A

B B B

A A C

C A C

B A C

A C A

B C C

B A A

C B C

A C C

A A C

C C B

B B C

B C C

A A B

B A A

A B B

A B A

C C C

C C A

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1 Answer 1

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Can machine learning algorithms predict random number

No, absolutely not.

ML is not a magical process, it works by identifying patterns in the data. In the case of supervised learning (i.e. with training) it means finding in the features the most relevant indications to help predict the target variable.

Randomness is the complete opposite of "patterns", by definition something truly random is not predictible in any way.

Intuitively if a human with a lot of time cannot do a task, then ML cannot do this task either.

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  • $\begingroup$ Wouldn't certain pseudo random number generators be (partially) deterministic? For a more exhaustive answer, and discussion, see related question: ai.stackexchange.com/questions/3850/… $\endgroup$ Apr 14, 2021 at 11:54
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    $\begingroup$ @MagnusPersson, thanks for the link. Sure, if the sequence is no truly random then ML might be able to catch some of the deterministic patterns. But either there are major non-random patterns, in which case the sequence is only random in name, or these deterministic patterns are minor and then the performance of any system trying to predict the sequence will be very low. $\endgroup$
    – Erwan
    Apr 14, 2021 at 14:00

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