Python PyCaret How to improve results from hyperparameter tuning by increasing "n_iter"














































Python PyCaret How to improve results from hyperparameter tuning by increasing "n_iter"




How to  improve results from hyperparameter tuning by increasing "n_iter"

 
The tune_model function in the pycaret.classification module and the pycaret.regression module employs random grid search over pre-defined grid search for hyper-parameter tuning. Here the default number of iterations is set to 10.

Results from tune_model may not necessarily be an improvement on the results from the base models created using create_model. Since the grid search is random, you can increase the n_iter parameter to improve the performance. See example below:

#tune with default n_iter i.e. 10
tuned_dt1 = tune_model('dt')

#tune with n_iter = 50
tuned_dt2 = tune_model('dt', n_iter = 50)

 

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