Hi there, I would like to fully understand how the Random Forest Regressor chooses how to split the data at each node.
I understand that each tree considers a boostrap sample of the training data, and on each split a random subset of features (using max_features) are considered. But among these features, how does the algorithm work out which is the best split to make? I am using the default criterion 'mse', but don't understand the given explanation "equal to variance reduction as feature selection criterion". Does this mean that for each possible split that could be made, the sum of variances of data in the child nodes is calculated, then the algorithm would use the split with the least sum of variances? Kind regards, Jonny Evans Doctoral Researcher Transportation Research Group Faculty of Engineering and the Environment University of Southampton Email: jonny.ev...@soton.ac.uk<mailto:jonny.ev...@soton.ac.uk>
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