1- I am using both fuctions to predict a response through random forest, but the predict function gives higher percentage of explained variance compared to oobPredict. Why is it so? – I think there is some fundamental thing that I have not yet fully grasped.
2- If there is something different between these methods in the way that they weigh trees how can I make these methods homogenous?
3- Can one use oobPredict in someway to make predictions with a new set of data?1- I am using both fuctions to predict a response through random forest, but the predict function gives higher percentage of explained variance compared to oobPredict. Why is it so? – I think there is some fundamental thing that I have not yet fully grasped.
2- If there is something different between these methods in the way that they weigh trees how can I make these methods homogenous?
3- Can one use oobPredict in someway to make predictions with a new set of data? 1- I am using both fuctions to predict a response through random forest, but the predict function gives higher percentage of explained variance compared to oobPredict. Why is it so? – I think there is some fundamental thing that I have not yet fully grasped.
2- If there is something different between these methods in the way that they weigh trees how can I make these methods homogenous?
3- Can one use oobPredict in someway to make predictions with a new set of data? random forest, regression, machine learning, curve fitting, decision trees, bagging, oob MATLAB Answers — New Questions

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