Millised võivad olla ohud andmete normaliseerimisel juhusliku metsa ekstrapolatsiooni probleemi lahendamisel?

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 extrapolation problem, the following pitfalls may arise:

• Distortion of data distribution: Normalization can alter the distribution of features, affecting the splitting of trees and consequently the model quality.
• Problems with new data: If normalization is based on training sample statistics, new data outside this range may be incorrectly scaled, worsening extrapolation.
• False sense of security: Since a random forest poorly extrapolates beyond 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 sample or using models that work better with extrapolation.