Accuracy score decreases after grid search for best parameters?
Maybe the algorithm's default parameter works best but girdsearch is skipping these parameters. You should check with default parameters or you can try different hyperparameter tuning methods.
I'm in an intro to machine learning course and after creating two decision trees (the second with best parameters after a grid search) the accuracy score decreased slightly. I have to explain why, but I'm confused why the score would go down if we're using the best parameters? The question is below:
Compare the accuracy score from the first Decision Tree to the accuracy score after you performed the grid search. How does it differ? It is most likely that you will find the accuracy score has decreased.  Is that what you had expected?  We perform a round of grid searching in order to elucidate the optimal hyperparameter values.  Why, then, has the accuracy score decreased?  Most importantly, what caused this decrease in the accuracy score and why?