Yes that’s exactly overfitting the bootstrapped samples, thus the high variance. The « variance » won’t just magically vanish as you average things out[1], you

Yes that’s exactly overfitting the bootstrapped samples, thus the high variance. The « variance » won’t just magically vanish as you average things out[1], you need to change the scale and check out the asymptotic law of your estimator (CLT, Kolmogorov-Smirnov… etc.) and confront it to your data. [1] the variance of the estimator itself vanishes thanks to LLN (in case of convergence), but that’s not actually the quantity of interest Edit: don't get me wrong, I'm not saying that RFs are good or bad, just reacting to the bias/variance thing.