How to do HyperParametertuning with GridSearchCV

In almost any Machine Learning project, we train different models on the dataset and select the one with the best performance. However, there is room for improvement as we cannot say for sure that this particular model is best for the problem at hand. Hence, our aim is to improve the model in any way possible. One important factor in the performances of these models are their hyperparameters, once we set appropriate values for these hyperparameters, the performance of a model can improve significantly. In this article, we will find out how we can find optimal values for the hyperparameters of a model by using GridSearchCV.

GridSearchCV is the process of performing hyperparameter tuning in order to determine the optimal values for a given model. As mentioned above, the performance of a model significantly depends on the value of hyperparameters. Note that there is no way to know in advance the best values for hyperparameters so ideally, we need to try all possible values to know the optimal values. Doing this manually could take a considerable amount of time and resources and thus we use GridSearchCV to automate the tuning of hyperparameters.

GridSearchCV is a function that comes in Scikit-learn’s(or SK-learn) model_selection package.So an important point here to note is that we need to have the Scikit learn library installed on the computer. This function helps to loop through predefined hyperparameters and fit your estimator (model) on your training set. So, in the end, we can select the best parameters from the listed hyperparameters.

 { 'C': [0.1, 1, 10, 100, 1000],  

   'gamma': [1, 0.1, 0.01, 0.001, 0.0001], 

   'kernel': ['rbf',’linear’,'sigmoid']  }

Here C, gamma and kernels are some of the hyperparameters of an SVM model. Note that the rest of the hyperparameters will be set to their default values

GridSearchCV tries all the combinations of the values passed in the dictionary and evaluates the model for each combination using the Cross-Validation method. Hence after using this function we get accuracy/loss for every combination of hyperparameters and we can choose the one with the best performance.

references:

https://www.mygreatlearning.com/blog/gridsearchcv/#:~:text=GridSearchCV%20is%20a%20technique%20for,parameter%20values%2C%20predictions%20are%20made.