What is the effect of subsample size on Random Forest variance and bias?

Smaller subsamples increase variance reduction but may increase bias.

Reducing bootstrap sample size increases diversity among trees, lowering correlation and variance. However, smaller samples may lead to weaker individual trees, increasing bias. This creates a tradeoff where optimal subsample size balances tree strength and ensemble diversity.