I am trying to build a Decision-Tree model on top of a dataset that is about 10G in size on my local computer. However, I only have 8G memory. What I am doing now is just random sampling certain subset of the data, trying different model parameters and checking the prediction performance with the scikit-learn package. Still I need a final model based on the model parameters from those random sampling results, but obviously I cannot build a model on top of the full dataset. What can I do with this problem? I am a newbie of machine learning, any suggestions are welcome. Thanks!
Predictive modeling on big data set that can't fit into memory
machine learning;python;scikit learn;decision trees