How does the class_weight parameter in scikit-learn work?

I am having a lot of trouble understanding how the class_weight parameter in scikit-learn’s Logistic Regression operates. The Situation I want to use logistic regression to do binary classification on a very unbalanced data set. The classes are labelled 0 (negative) and 1 (positive) and the observed data is in a ratio of about 19:1 with the majority of samples having negative outcome. First Attempt: Manually Preparing Training Data I split the data I had into disjoint sets for training and testing (about 80/20). Then I randomly sampled the training data by hand to get training data in different proportions

RandomForestClassifier import

I’ve installed Anaconda Python distribution with scikit-learn. While importing RandomForestClassifier: from sklearn.ensemble import RandomForestClassifier I have the following error: File “C:Anacondalibsite-packagessklearntreetree.py”, line 36, in <module> from . import _tree ImportError: cannot import name _tree What the problem can be there? Answer The problem was that I had the 64bit version of Anaconda and the 32bit sklearn.