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I have a modeling framework with an outcome that must necessarily be positive. In the training data, the outcome ranges from close to zero to much higher (approximately 0.05 to 100). Is there a way to fit a random forest model for positive outcomes? Prediction accuracy is important in this problem, and I want to model the data with more flexible, nonparametric methods like random forests.

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    $\begingroup$ A Random Forest can not predict outside of the range of your target variable. This is why trees are typically looked down upon when there is a significant trend for example in time series. $\endgroup$
    – Tylerr
    Commented May 10, 2021 at 13:55

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Random forest does this by default. Random forest predictions are the average of the terminal node values. The average of nonnegative values is also nonnegative.

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