Python PyCaret Ensemble Model














































Python PyCaret Ensemble Model




Ensemble Model-PyCaret

 
There are few functions available to ensemble base learners. ensemble_model, blend_models and stack_models are three of them. 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')

# train a bagging classifier on dt
bagged_dt = ensemble_model(dt, method = 'Bagging')

# train a adaboost classifier on dt with 100 estimators
boosted_dt = ensemble_model(dt, method = 'Boosting', n_estimators = 100)

# train a votingclassifier on all models in library
blender = blend_models()

# train a voting classifier on specific models
dt = create_model('dt')
rf = create_model('rf')
adaboost = create_model('ada')
blender_specific = blend_models(estimator_list = [dt,rf,adaboost], method = 'soft')

# train a voting classifier dynamically
blender_top5 = blend_models(compare_models(n_select = 5))

# train a stacking classifier
stacker = stack_models(estimator_list = [dt,rf], meta_model = adaboost)

# stack multiple models dynamically
top7 = compare_models(n_select = 7)
stacker = stack_models(estimator_list = top7[1:], meta_model = top7[0])

Predict Model

 
As the name suggests, this function is used for inference / prediction. Here is how you can use it:

# train a catboost model
catboost = create_model('catboost')

# predict on holdout set (when no data is passed)
pred_holdout = predict_model(catboost)

# predict on new dataset
new_data = pd.read_csv('new-data.csv')
pred_new = predict_model(catboost, data = new_data)

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