Python PyCaret Create Model














































Python PyCaret Create Model




Create Model-PyCaret

 
Create Model function trains a model using default hyperparameters and evaluates performance metrics using cross validation. This function is base to almost all other functions in PyCaret. It returns the trained model object class. 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 logistic regression model
lr = create_model('lr') #lr is the id of the model
# check the model library to see all models
models()
# train rf model using 5 fold CV
rf = create_model('rf', fold = 5)
# train svm model without CV
svm = create_model('svm', cross_validation = False)
# train xgboost model with max_depth = 10
xgboost = create_model('xgboost', max_depth = 10)
# train xgboost model on gpu
xgboost_gpu = create_model('xgboost', tree_method = 'gpu_hist', gpu_id = 0) #0 is gpu-id
# train multiple lightgbm models with n learning_rate
lgbms = [create_model('lightgbm', learning_rate = i) for i in np.arange(0.1,1,0.1)]
# train custom model
from gplearn.genetic import SymbolicClassifier
symclf = SymbolicClassifier(generation = 50)
sc = create_model(symclf)

Sample Output:

Figure

Sample output from create_model function

Happy Pythoning....!!

 


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