Data Science Asked on April 2, 2021
I am building a machine learning based model (random forest in scikit-learn) to predict maize yields in the U.S. based on data on historical maize yields and temperature and precipitation information. Since there is a trend in maize yields, I detrend the yield data first, then fit the model and then predict for current year. However, I assume that the predictions from the model are detrended as well. How do I go about making predictions that are not detrended? Any pointers and literature reference would be helpful
I'm just guessing but I can think of two options:
Answered by Erwan on April 2, 2021
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