CODEWITHSUNDEEP
pythonmachine-learningh2o
Daniele
Daniele

Reputation: 21

h2o python balance classes

I'm having problems implementing a simple balancing for an H2ORandomForestEstimator, I'm trying to reproduce a simple example found in Darren Cook's book written in R ('Practical Machine Learning with H2O - pag. 107).

Working on the Iris Dataset, firstly I artificially unbalance the target variable cutting out a good share of virginica keeping first 120 rows.

Then I build 3 models, a vanilla one, one where I set balance_classes as True, and a last one where I set balance_classes as True and I input a list for class_sampling_factors to oversample the virginica one. List is [1.0,1.0,2.5], referred to columns sorted alphabetically.

I train them, and then output confusion matrix for train for each one.

I'm expecting an unbalanced output for the first one, and a balanced one for the last two, while I have always the same result. I checked the documentation example in Python, and I can't see anything wrong (I may be tired as well).

This is my code:

data_unb = data[1:120,:]  # messing up with target variable
train, valid = data_unb.split_frame([0.8], seed=12345)

m1 = h2o.estimators.random_forest.H2ORandomForestEstimator(seed=12345)
m2 = h2o.estimators.random_forest.H2ORandomForestEstimator(balance_classes=True, seed=12345)
m3 = h2o.estimators.random_forest.H2ORandomForestEstimator(balance_classes=True, class_sampling_factors=[1.0,1.0,2.5], seed=12345)

m1.train(x=list(range(4)),y=4,training_frame=train,validation_frame=valid,model_id='RF_defaults')
m2.train(x=list(range(4)),y=4,training_frame=train,validation_frame=valid,model_id='RF_balanced')
m3.train(x=list(range(4)),y=4,training_frame=train,validation_frame=valid,model_id='RF_class_sampling',)

m1.confusion_matrix(train)
m2.confusion_matrix(train)
m3.confusion_matrix(train)

This is my output:

my confusion matrices (wrong)

this is my expected output.

expected confusion matrices

What am I evidently missing? Thanks in advance.

Upvotes: 2

Views: 1095

Answers (1)

Erin LeDell
Erin LeDell

Reputation: 8819

You're not missing anything. The offset_column is available in H2O Random Forest, but it's not actually functional. The bug is documented here and should be fixed in the next stable release of H2O. Sorry about the confusion!

It should work for the rest of the H2O algos (except XGBoost). If you wanted to try on a GBM, for example, you'd see it working.

Upvotes: 2

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