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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