Multi variant AB testing vs Multi-Armed bandit

Multi-variant: 3% vs 5% vs 7%

Payoff

Payoff from variation 3% vs 5% vs 7%

Payoff from variation 3% vs 5% vs 7%

You can probably see a pattern here already. The Epsilon greedy comes out winning in terms of rewards again followed by UCB1 and AB test with a bigger difference now (16% and 21% respectively).

Certainty

The difference is big enough between the variations to reach confidence quite quickly. UCB1 is the fastest followed by Epsilon greedy and AB.

Confidence for 3% vs 5% vs 7%

Confidence for 3% vs 5% vs 7%

Run behaviour

Variations display in AB model

Variations display in AB model

Variations display in Epsilon greedy model

Variations display in Epsilon greedy model

Variations display in UCB1 model

Variations display in UCB1 model

No surprise there. What’s worth to mention is the Epsilon greedy ability to come back from the bad start much quicker than it was in the previous example mostly because the difference is much bigger.

The UCB1 is mostly serving the best performing version probing the second time to time and running the worse on low frequency.

Stopping when confidence level reached

Reaching confidence quite fast again we can check what would have happened if we had stopped when reached 95%.

Payoff from variation 3% vs 5% vs 7% if stopped at confidence level

Payoff from variation 3% vs 5% vs 7% if stopped at confidence level

In this case UCB1 comes out first because it’s ability to quickly reach the point when we can switch to full exploit. It’s providing 4.5% higher reward than AB and ~1% more than Epsilon.

About charlesnagy

I'm out of many things mostly automation expert, database specialist, system engineer and software architect with passion towards data, searching it, analyze it, learn from it. I learn by experimenting and this blog is a result of these experiments and some other random thought I have time to time.
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