How can i save a machine learning model that takes less memory

Question

I am training a RandomForest Classifier with somewhat large dataset of around 580mb and it is taking more than 30 min to fit. Now when I try to save the model using joblib then the saved model takes around 11.1gb of space. Is it normal or I can save the model more efficiently in terms of space consumed as I am thinking of deploying the model.

Is it worth using a model that takes so much space because I have a decision tree model on same data that takes 278mb space and it's accuracy is just 2% lower(91%)

my notebook

This is model saving code

from sklearn.externals import joblib  
# Save the model as a pickle in a file 
joblib.dump(Random_classifier, '/content/drive/My Drive/Random_classifier.pkl') 

I am a newbie so don't vote to close the question just leave a comment. I am willing to edit the question asap.

Answer 1

Random Forest classification method is way expensive in memory. try to lower your decision trees number, might reduce some memory. It seems your dataset is also very big so I think it seems legit your weights size. Also I know there is pickle way to save weights, I would recommend to check it out too.