Thoughts on hyperparameter tuning using sklearn's GridSearchCV

Hi guys,

I just wanted to ask a question to those who have more experience here.

When I got taught about hyperparameter tuning using GridSearchCV (and other SearchCV tools), the focus always was on maximising the score(s) chosen for the model at hand. However when I get test scores of 0.95 or over, I start getting nervous. Am I overfitting the data?

Since GridSearchCV utilises cross validation and all the training is done with data *separate* from the one used …

datascience gridsearchcv hyperparameter sklearn