tune_model.py

# 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]