# How is the Weighted Gini Criterion defined?

I am interested in trying out and/or implementing the Weighted Random Forest (WRF) algorithm described in Chen, Liaw, Breiman. How is the Weighted Gini impurity actually defined? What implementations of the algorithm exist?

My best guess would be that the weighted Gini impurity is defined by

$$\sum_i w_i * f_i * (1-f_i)$$

where $i$ is the class index, $w_i$ is the class weight, and $f_i$ is the fraction of elements in the group of class $i$. And that splits at each node are chosen by minimizing the weighted average sum of these impurities.

The implementation available at Breiman's website uses the weighted random forest method described in the paper. It's in Fortran 77 though, which may be off-putting to you. I also found the method that they use to calculate the impurity to be more unclear and complicated than needs be, but that's just my opinion (and probably an artifact of the method that they use to grow their trees).

For any potential split, the weight of all the observations in a potential child node, $c$, is

$$t_c = \sum_i w_i * n_i$$

where $n_i$ is the number of observations of class $i$ in $c$, and $w_i$ is the weight assigned to class $i$. The impurity of child node $c$ is then

$$i_c = 1 - \sum_i (w_i * n_i / t_c)^2$$

where $n_i$ is again the number of observations of class $i$ in the node, $w_i$ is the weight assigned to the class and $t_c$ is as calculated previously.

The impurity of the entire potential split is then

$$\sum_c(t_c / t_p) * i_c$$

where $t_c$ and $i_c$ are as calculated previously, and $t_p$ is the total weight of all observations in the parent node that is being split. Basically the fraction of the parent node's total weight that is in each child node $c$ multiplied by $c$'s impurity. You then choose the potential split that has the lowest impurity over all potential splits.

• Shouldn't $i_c$ be $1 - \sum_i (w_i n_i / t_c)^2$? if you increase the weights too much the impurity goes to 1. – Simone Sep 5 '13 at 4:55
• You're right. I seem to always make typos when converting code into mathematical expressions. I've fixed it now. – user28504 Sep 5 '13 at 10:46

Just a follow-up on Simon's answer.

The Gini index (impurity index) for a node $$c$$ can be defined as: $$i_c = \sum_i f_i \cdot ( 1- f_i) = 1 - \sum_i f_i^2$$ where $$f_i$$ is the fraction of records which belong to class $$i$$.

If we have a two class problem we can plot the Gini index varying the relative number of records of the first class $$f$$. That is $$f_1 = f$$ and $$f_2 = f - f_1$$.

We have very high impurity when the class distribution is uniform.

If we assign a weight to each class we are actually rebalancing the impurity index - a high weight for one class (say $$\bar{i}$$) leads to a stronger decrease of impurity when the number of records for that class increases. We will not have the maximum impurity when the class distribution is uniform anymore. Just a small amount of records in $$\bar{i}$$ will be enough to decrease the node impurity.

We can still sketch a two class problem example. In Simon's formulas our $$f_i$$s are $$f_i = \frac{w_i \cdot n_i}{\sum_i w_i \cdot n_i}$$ We can see that the formula does not change if $$\sum_i n_i = 1$$ and $$\sum_i w_i = 1$$ (it is enough to multiply numerator and denominator by the total number of records and total weight). Let $$f$$ be $$f = \frac{w \cdot n}{w \cdot n + (1-w)(1-n)}$$ where $$n$$ is the relative number of records for the first class and $$w$$ their weight. Now we could compute the Gini when we vary the relative number of records for the first class $$n$$.

For example if we choose a weight of $$0.9$$ we have maximum impurity when $$n \approx 0.1$$. If $$n$$ increases the impurity steeply decreases as well.

If we define the weigthed Gini index as the OP did we would for example have a strange behaviour in a two class problem: $$\sum_i w_i \cdot f_i \cdot (1 - f_i) = w \cdot f \cdot (1 - f) + (1 - w) \cdot (1-f) \cdot f = f \cdot ( 1- f)$$ However, I found a paper that uses that index here but I didn't read it, so I might be wrong.

Breiman's weighted index makes actually sense - we are trying to reweight the classes in a cost-sensitive way. The same approach is used in the MetaCost classifier implemented in WEKA.

In case anyone is interested, this is the R code I used to plot the figures:

require(ggplot2)

# two class probability distribution
f <- seq(0,1,0.01)

# Standard Gini impurity
# f1 = f and f2 = 1 - f
Gini <- f*(1-f) + (1-f)*f
qplot(f,Gini, geom = "path")

# Weighted Gini impurity
w <- seq(0,1,0.1)
w_ <- as.vector( sapply(w, function(x) rep(x,length(f))))
n <- rep(f,length(w))

f <- w_*n / ( w_*n + (1-w_)*(1-n) )
WeightedGini <- f*(1-f) + (1-f)*f

Weight <- as.factor(w_)
qplot(n, WeightedGini, colour = Weight, geom = "path")