# Is feature importance in sklearn the same as proposed by Breiman 1984?

Breiman 1984 feature importance of a variable $$j$$ for a regression tree $$T$$ is:

Sklearn regression tree feature importance can be get as follows:

Where $$\hat{l}_t^2$$ is MSE improvement after splitting a such node $$t$$ and $$v_t$$ is the variable used to split that node. The summation means how much the variable $$j$$ decreased the impurity of some tree $$T$$. Is this equivalent (if normalized) to sklearn Feature importance ? Breiman hasn't used "Gini importance" to describe feature importance like sklearn has done, Would the expression "gini importance" be just a way to name it on normalized case?