Python PyCaret How to improve results from hyperparameter tuning by increasing "n_iter"














































Python PyCaret How to improve results from hyperparameter tuning by increasing "n_iter"




How to  improve results from hyperparameter tuning by increasing "n_iter"

 
The tune_model function in the pycaret.classification module and the pycaret.regression module employs random grid search over pre-defined grid search for hyper-parameter tuning. Here the default number of iterations is set to 10.

Results from tune_model may not necessarily be an improvement on the results from the base models created using create_model. Since the grid search is random, you can increase the n_iter parameter to improve the performance. See example below:

#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)

 

Image for post

 


More Articles of Aditi Kothiyal:

Name Views Likes
Python AdaBoost Mathematics Behind AdaBoost 477 1
Python PyCaret How to optimize the probability threshold % in binary classification 2357 0
Python K-means Predicting Iris Flower Species 1425 2
Python PyCaret How to ignore certain columns for model building 3438 0
Python PyCaret Experiment Logging 911 0
Python PyWin32 Open a File in Excel 1237 0
Python Guppy GSL Introduction 271 2
Python Usage of Guppy With Example 1253 2
Python Naive Bayes Tutorial 613 2
Python Guppy Recent Memory Usage of a Program 1007 2
Introduction to AdaBoost 363 1
Python AdaBoost Implementation of AdaBoost 570 1
Python AdaBoost Advantages and Disadvantages of AdaBoost 4168 1
Python K-Means Clustering Applications 399 2
Python Random Forest Algorithm Decision Trees 501 0
Python K-means Clustering PREDICTING IRIS FLOWER SPECIES 576 1
Python Random Forest Algorithm Bootstrap 565 0
Python PyCaret Util Functions 512 0
Python K-means Music Genre Classification 1937 1
Python PyWin Attach an Excel file to Outlook 1844 0
Python Guppy GSL Document and Test Example 323 2
Python Random Forest Algorithm Bagging 458 0
Python AdaBoost An Example of How AdaBoost Works 355 1
Python PyWin32 Getting Started PyWin32 1540 0
Python Naive Bayes in Machine Learning 434 2
Python PyCaret How to improve results from hyperparameter tuning by increasing "n_iter" 1889 0
Python PyCaret Getting Started with PyCaret 2.0 417 1
Python PyCaret Tune Model 1714 1
Python PyCaret Create your own AutoML software 400 0
Python PyCaret Intoduction to PyCaret 357 1
Python PyCaret Compare Models 3314 1
Python PyWin Copying Data into Excel 1329 0
Python Guppy Error: expected function body after function declarator 481 2
Python Coding Random forest classifier using xgBoost 296 0
Python PyCaret How to tune "n parameter" in unsupervised experiments 736 0
Python PyCaret How to programmatically define data types in the setup function 1498 0
Python PyCaret Ensemble Model 932 1
Python Random forest algorithm Introduction 266 0
Python k-means Clustering Example 398 1
Python PyCaret Plot Model 1460 1
Python Hamming Distance 812 0
Python Understanding Random forest algorithm 355 0
Python PyCaret Sort a Dictionary by Keys 301 0
Python Coding Random forest classifier using sklearn 413 0
Python Guppy Introduction 458 2
Python How to use Guppy/Heapy for tracking down Memory Usage 1205 2
Python AdaBoost Summary and Conclusion 285 1
Python PyCaret Create Model 428 1
Python k -means Clusturing Introduction 399 2
Python k-means Clustering With Example 409 2

Comments