Python PyCaret Tune Model














































Python PyCaret Tune Model




 Tune Model

 
Tune Model function tunes the hyperparameter of the model passed as an estimator. It uses Random grid search with pre-defined tuning grids that are fully customizable. Here are few ways you can use this function:

# import classification module
from pycaret.classification import *
# init setup
clf1 = setup(data, target = 'name-of-target')
# train a decision tree model
dt = create_model('dt')
# tune hyperparameters of decision tree
tuned_dt = tune_model(dt)
# tune hyperparameters with increased n_iter
tuned_dt = tune_model(dt, n_iter = 50)
# tune hyperparameters to optimize AUC
tuned_dt = tune_model(dt, optimize = 'AUC') #default is 'Accuracy'
# tune hyperparameters with custom_grid
params = {"max_depth": np.random.randint(1, (len(data.columns)*.85),20),
"max_features": np.random.randint(1, len(data.columns),20),
"min_samples_leaf": [2,3,4,5,6],
"criterion": ["gini", "entropy"]
}
tuned_dt_custom = tune_model(dt, custom_grid = params)
# tune multiple models dynamically
top3 = compare_models(n_select = 3)
tuned_top3 = [tune_model(i) for i in top3]


OUTPUT:



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