Thanks, so when I understand correctly, the criterion argument described as
"The function to measure the quality of a split. The only supported criterion 
is “mse” for the mean squared error."
only reports the quality but does not influence the split ... 

Maybe the use of variance reduction, or variance as impurity measure for the 
child nodes should be added somewhere to avoid confusion.

> On Jul 9, 2015, at 1:23 PM, Jacob Schreiber <jmschreibe...@gmail.com> wrote:
> 
> It is using the variance reduction algorithm to make the splits while the 
> tree is being built. The final tree can be evaluated using the Mean Squared 
> Error. 
> 
> On Thu, Jul 9, 2015 at 8:56 AM, Sebastian Raschka <se.rasc...@gmail.com> 
> wrote:
> Hi, all,
> 
> sorry, but I have another question regarding the terminology in the 
> documentation.
> 
> In the DecisionTreeRegressor's documentation at
> http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html#sklearn.tree.DecisionTreeRegressor
> is says
> 
> criterion : string, optional (default=”mse”)
> The function to measure the quality of a split. The only supported criterion 
> is “mse” for the mean squared error.
> 
> However, I am wondering if the impurity measure is truly the MSE or if it is 
> the variance of the nodes (since the wikipedia link on that page refers to 
> the "variance reduction" algorithm)? Here, I think of MSE as the average of 
> squared deviations of the predictions from the true values, whereas variance 
> would be the average of squared deviation of the observations from the sample 
> mean of a node.
> 
> Best,
> Sebastian
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