How does random Forest work for regression? I am an absolute beginner in field of machine learning, I started doing titanic assignment in Kaggle and found(read some where) Random Forest is the best fit. I started reading about random forest and found the Explanation by Edwin Chen in this  question intuitive. This made me "understand" how I can solve the Titanic assignment which predicts if one survives or not(classification). But I cannot understand How random Forest will work for regression which is continuous. 
Please don't mind to point out any mistakes in my assumptions or the way I started things. Any advice would be helpful, This looks very vast and Don't even know where to begin.
 A: Basically there are two differences:


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*when building the model/tree it is used a different criteria to split information; for example on binary split the purpose is to choose the split variable and split value so that the sum of variances of the two resulting data points on target/output variable is minimal

*when you predict values you will use the mean value of the target/output variable for all data points in the leaf node


Some variants I saw:


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*for splitting you might want to minimize the sum of standard deviations, the weighed sum of variances, etc

*for prediction values you can also use trimmed mean, median or even another model (like a linear model fitted on instances from the node)

A: Ignore the forest part for a moment, even a single tree can do regression. Each leaf holds a prediction value, which no longer is a class for regression. Given an input feature vector, you simply walk the tree as you'd do for a classification problem, and the resulting value in the leaf node is the prediction. 
For a forest, simply averaging the prediction of each tree is valid, although you may want to investigate if that's sufficiently robust for your application.
