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Can anybody explain why/if target variable transformations could help when dealing with tree based models?

I've seen this excellent reply which explains quite well why it shouldn't affect if transforming inputs, but I haven't been able to find anything regarding outputs.

Can using a transformation like taking logs or using quantile transform of the response variable help?

Actually, we are using XGBoost and getting better results when using a normal quantile transform of our output and even better results when taking logs (our response variable is a price, highly skewed to the right) but I don't know if this is something justifiable by theory or just random chance.

Can anybody explain why/if target variable transformations could help when dealing with tree based models?

I've seen this excellent reply which explains quite well why it shouldn't affect if transforming inputs, but I haven't been able to find anything regarding outputs.

Can using a transformation like taking logs or using quantile transform of the response variable help?

Actually, we are using XGBoost and getting better results when using a normal quantile transform of our output and even better results when taking logs (our response variable is a price, highly skewed to the right) but I don't know if this is something justifiable by theory or just random chance.

Can anybody explain why/if target variable transformations could help when dealing with tree based models?

I've seen this excellent reply https://datascience.stackexchange.com/a/5278/63445 which explains quite well why it shouldn't affect if transforming inputs, but I haven't been able to find anything regarding outputs.

Can using a transformation like taking logs or using quantile transform of the response variable help?

Actually, we are using XGBoost and getting better results when using a normal quantile transform of our output and even better results when taking logs (our response variable is a price, highly skewed to the right) but I don't know if this is something justifiable by theory or just random chance.

Thanks in advance

Can anybody explain why/if target variable transformations could help when dealing with tree based models?

I've seen this excellent reply which explains quite well why it shouldn't affect if transforming inputs, but I haven't been able to find anything regarding outputs.

Can using a transformation like taking logs or using quantile transform of the response variable help?

Actually, we are using XGBoost and getting better results when using a normal quantile transform of our output and even better results when taking logs (our response variable is a price, highly skewed to the right) but I don't know if this is something justifiable by theory or just random chance.

Can anybody explain why/if target variable transformations could help when dealing with tree based models?

I've seen this excellent reply https://datascience.stackexchange.com/a/5278/63445 which explains quite well why it shouldn't affect if transforming inputs, but I haven't been able to find anything regarding outputs.

Can using a transformation like taking logs or using quantile transform of the response variable help?

Thanks in advance

Can anybody explain why/if target variable transformations could help when dealing with tree based models?

I've seen this excellent reply https://datascience.stackexchange.com/a/5278/63445 which explains quite well why it shouldn't affect if transforming inputs, but I haven't been able to find anything regarding outputs.

Can using a transformation like taking logs or using quantile transform of the response variable help?

Actually, we are using XGBoost and getting better results when using a normal quantile transform of our output and even better results when taking logs (our response variable is a price, highly skewed to the right) but I don't know if this is something justifiable by theory or just random chance.

Thanks in advance