See Custom refit strategy of a gridsearch with cross-validation to see how to design a custom selection strategy using a callable via refit. See this example for an example of how to use refit=callable to balance model complexity and cross-validated score.
One method is to try out different values and then pick the value that gives the best score. This technique is known as a gridsearch. If we had to select the values for two or more parameters, we would evaluate all combinations of the sets of values thus forming a grid of values.
Implementation: GridSearching can be applied to any hyperparameters algorithm whose performance can be improved by tuning hyperparameter. For example, we can apply gridsearching on K-Nearest Neighbors by validating its performance on a set of values of K in it.
Let’s learn to optimize the model parameters with Scikit-Learn GridSearchCV. First, let us install the Pandas and Scikit-Learn packages if you haven’t had any installed in your environment. Let’s import the Python packages used in this tutorial. Next, we would create our sample data. For this tutorial, we will use the Iris dataset example.
This blog post will dive deep into gridsearch examples in Python, covering fundamental concepts, usage methods, common practices, and best practices. By the end of this guide, you'll be well-equipped to apply gridsearch effectively in your own machine learning projects.
In this short tutorial, we have seen how to implement and use a grid search to tune the hyperparameters of a ML model.