FarmView: Regression Analysis of 2016 Sorghum Composition
نویسنده
چکیده
Methods. I trained a random forest, and other regressors, on the 2016 dataset using 10-fold cross validation. I augmented the training set so there were more training samples with missingness identical to the missingness in the test set. Finally, I trained on three different views of the dataset to determine the incremental increase in predictive power from the robot-generated plus dronegenerated features, as well as the harvest phenotypes. Results The random forest regressor preformed the best with an overall r2 score of 0.436. This includes the 2.5% increase in r2 score from my missing value augmentation
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