What pitfalls can occur when normalizing data to solve the extrapolation problem in a random forest?

When normalizing data for a random forest, it is important to remember that this algorithm is not sensitive to feature scale, as it is based on decision trees. However, if normalization is applied to address the problem of extrapolation, the following pitfalls may arise:

• Data distribution distortion: Normalization can alter the distribution of features, which will affect tree splitting and, consequently, the model quality.
• Problems with new data: If normalization is based on the statistics of the training set, new data outside this range may be scaled incorrectly, worsening extrapolation.
• False sense of security: Since a random forest poorly extrapolates beyond the training data, normalization will not solve this problem and may only hide it.

It is better to use normalization consciously and remember that to improve the extrapolation of a random forest, other methods should be considered, such as expanding the training set or using models that work better with extrapolation.