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Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret

#artificialintelligence

Here's a situation every PyCaret user is familiar with: after selecting a promising model or two from compare_models(), it's time to tune its hyperparameters to squeeze out all of the model's potential with tune_model(). By default, tune_model() uses the tried and tested RandomizedSearchCV from scikit-learn. However, not everyone knows about the various advanced options tune_model()provides. In this post, I will show you how easy it is to use other state-of-the-art algorithms with PyCaret thanks to tune-sklearn, a drop-in replacement for scikit-learn's model selection module with cutting edge hyperparameter tuning techniques. I'll also report results from a series of benchmarks, showing how tune-sklearn is able to easily improve classification model performance.


Bayesian Hyperparameter Optimization with tune-sklearn in PyCaret - KDnuggets

#artificialintelligence

Here's a situation every PyCaret user is familiar with: after selecting a promising model or two from compare_models(), it's time to tune its hyperparameters to squeeze out all of the model's potential with tune_model(). By default, tune_model() uses the tried and tested RandomizedSearchCV from scikit-learn. However, not everyone knows about the various advanced options tune_model()provides. In this post, I will show you how easy it is to use other state-of-the-art algorithms with PyCaret thanks to tune-sklearn, a drop-in replacement for scikit-learn's model selection module with cutting edge hyperparameter tuning techniques. I'll also report results from a series of benchmarks, showing how tune-sklearn is able to easily improve classification model performance.


How to Do Hyperparameter Tuning on Any Python Script in 3 Easy Steps

#artificialintelligence

You wrote a Python script that trains and evaluates your machine learning model. Now, you would like to automatically tune hyperparameters to improve its performance? In this article, I will show you how to convert your script into an objective function that can be optimized with any hyperparameter optimization library. It will take just 3 steps and you will be tuning model parameters like there is no tomorrow. Take the parameters that you want to tune and put them in a dictionary at the top of your script.