What initial bias and variance do Random Forests typically have?

Random Forests generally exhibit low bias and high variance, especially at their initial configuration. This characteristic arises from the ensemble nature of Random Forests, which combines multiple decision trees to improve the overall accuracy of predictions. Decision trees, as individual models, can be prone to overfitting the training data, leading to high variance.

In the case of Random Forests, while individual trees might show high variance due to their complexity and tendency to capture noise in the training data, the ensemble approach helps in reducing the overall variance through the averaging of predictions. However, at the beginning stages before any regularization techniques or tree pruning are employed, they still maintain a relatively high variance compared to other simpler models.

This is in contrast to models that have high bias, which simplify the data relationships too much and may underfit the data, or models that start unbiased but evolve with training, which does not accurately describe how Random Forests operate as an ensemble method. Thus, the assertion that Random Forests start with low bias and high variance aligns well with their statistical properties and behavior during initial training phases.