Unfortunately (or maybe fortunately :)) no, maximizing variance reduction &
minimizing MSE are just special cases :)
Best,
Sebastian
> On Mar 1, 2018, at 9:59 AM, Thomas Evangelidis wrote:
>
> Does this generalize to any loss function? For example I also want to
> implement Kendall's tau corr
Does this generalize to any loss function? For example I also want to
implement Kendall's tau correlation coefficient and a combination of R, tau
and RMSE. :)
On Mar 1, 2018 15:49, "Sebastian Raschka" wrote:
> Hi, Thomas,
>
> as far as I know, it's all the same and doesn't matter, and you would
Hi, Thomas,
as far as I know, it's all the same and doesn't matter, and you would get the
same splits, since R^2 is just a rescaled MSE.
Best,
Sebastian
> On Mar 1, 2018, at 9:39 AM, Thomas Evangelidis wrote:
>
> Hi Sebastian,
>
> Going back to Pearson's R loss function, does this imply th
Hi Sebastian,
Going back to Pearson's R loss function, does this imply that I must add an
abstract "init2" method to RegressionCriterion (that's where MSE class
inherits from) where I will add the target values X as extra argument? And
then the node impurity will be 1-R (the lowest the best)? What
Hi, Thomas,
in regression trees, minimizing the variance among the target values is
equivalent to minimizing the MSE between targets and predicted values. This is
also called variance reduction:
https://en.wikipedia.org/wiki/Decision_tree_learning#Variance_reduction
Best,
Sebastian
> On Mar 1
Hi again,
I am currently revisiting this problem after familiarizing myself with
Cython and Scikit-Learn's code and I have a very important query:
Looking at the class MSE(RegressionCriterion), the node impurity is defined
as the variance of the target values Y on that node. The predictions X are
