Hyper parameter Tuning (GridSearchCV)


In this python machine learning tutorial for beginners we will look into,
1) how to hyper tune machine learning model paramers 
2) choose best model for given machine learning problem
We will start by comparing traditional train_test_split approach with k fold cross validation. Then we will see how GridSearchCV helps run K Fold cross validation with its convenient api. GridSearchCV helps find best parameters that gives maximum performance. RandomizedSearchCV is another class in sklearn library that does same thing as GridSearchCV
but without running exhaustive search, this helps with computation time and resources. We will also see how to find best model among all the classification algorithm using GridSearchCV. In the end we have interesting exercise for you to solve.
Exercise: https://github.com/codebasics/py/blob/master/ML/15_gridsearch/exercise.md
Code in this tutorial: https://github.com/codebasics/py/blob/master/ML/15_gridsearch/15_grid_search.ipynb

Topics that are covered in this Video:

00:45 train_test_split to find model performance
01:37 K fold cross validation
04:44 GridSearchCV for hyperparameter tuning
10:18 RandomizedSearchCV
12:35 Choosing best model
15:25 Exercise