pycaret

# 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')

# train a bagging classifier on dt

pycaret

# 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

pycaret

# import classification module
from pycaret.classification import *

# init setup
clf1 = setup(data, target = 'name-of-target')

# train adaboost model
adaboost = create_model('ada')

# AUC plot

pycaret

# import classification module
from pycaret.classification import *

# init setup
clf1 = setup(data, target = 'name-of-target')

# train logistic regression model
lr = create_model('lr') #lr is the id of the model

# check the model library to see all models

pycaret

# import classification module
from pycaret.classification import *

# init setup
clf1 = setup(data, target = 'name-of-target')

# return best model
best = compare_models()

# return best model based on Recall

pycaret

predict_model(xgboost, probability_threshold=0.2)

pycaret

# Importing dataset
from pycaret.datasets import get_data

credit = get_data('credit')
# Importing module and initializing setup
from pycaret.classification import *
clf1 = setup(data = credit, target = 'default')

# create a model
xgboost = create_model('xgboost')

pycaret

tuned_lda = tune_model(model='lda', supervised_target='status', estimator='xgboost')

pycaret

#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)

pycaret

# import regression module
from pycaret.regression import *

# init setup
reg1 = setup(data, target = 'charges', silent=True, 
            categorical_features=['sex', 'smoker', 'region', 'children'], 
            numeric_features=['age', 'bmi'])