How to tune hyper-parameters using RandomSearchCV in Python
When building a machine learning model, it’s important to optimize the parameters of the model for the best performance. One way to do this is by tuning the hyper-parameters using RandomSearchCV.
RandomSearchCV is a method that allows you to search for the best combination of hyper-parameters, by training and evaluating a model using random combinations of hyper-parameters. It will pick the best combination of hyper-parameters based on the performance of the model.
In Python, the library scikit-learn provides an easy way to perform RandomSearchCV using the function
The first step is to import the library and load the dataset into a pandas dataframe. Then, split the data into training and testing sets, and create an instance of the model you want to evaluate.
After that, you can use the
RandomSearchCV() function, which takes the model, the dataset, a dictionary of hyper-parameters and their possible values, and the scoring metric as inputs. The function returns the best combination of hyper-parameters based on the performance of the model.
You can use the
n_iter parameter that controls the number of combinations of hyper-parameters that will be tried.
Additionally, you can use the
refit parameter, it will refit the estimator with best hyper-parameters using all available data.
In summary, RandomSearchCV is a powerful tool for tuning the hyper-parameters of a machine learning model. By using the RandomSearchCV function in scikit-learn, it’s easy to tune the hyper-parameters in Python, making it a valuable tool for data scientists and machine learning practitioners. Keep in mind that, random search might not be as efficient as grid search in terms of time but it has been shown to be more efficient in finding better hyperparameters, especially when the search space is large.
In this Learn through Codes example, you will learn: How to tune hyper-parameters using RandomSearchCV in Python.
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