I have a dataset where the classes are unbalanced. The classes are either '1' or '0' where the ratio of class '1':'0' is 5:1. How do you calculate the prediction error for each class and the rebalance weights accordingly in sklearn with Random Forest, kind of like in the following link: http://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#balance
You can pass sample weights argument to Random Forest fit method
sample_weight : array-like, shape = [n_samples] or None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.
In older version there were a
preprocessing.balance_weights method to generate balance weights for given samples, such that classes become uniformly distributed. It is still there, in internal but still usable preprocessing._weights module, but is deprecated and will be removed in future versions. Don't know exact reasons for this.
Some clarification, as you seems to be confused.
sample_weight usage is straightforward, once you remember that its purpose is to balance target classes in training dataset. That is, if you have
X as observations and
y as classes (labels), then
len(X) == len(y) == len(sample_wight), and each element of
sample witght 1-d array represent weight for a corresponding
(observation, label) pair. For your case, if
1 class is represented 5 times as
0 class is, and you balance classes distributions, you could use simple
sample_weight = np.array([5 if i == 0 else 1 for i in y])
assigning weight of
5 to all
0 instances and weight of
1 to all
1 instances. See link above for a bit more crafty
balance_weights weights evaluation function.