To optimise hyperparameter of ML Model using F1

To optimise hypeparameter of ML Model based on different evaluation metrics (Accuracy, Recall, Precision, F1, F2, F0.5)

Grid search, Random search and Bayesian optimization are popular approaches to find the best combinations of parameter of Machine Learning model, cross validate each and determine which one gives the best performance.

This example will also discuss about how to fine tune the hyperparameter based on different evaluation metrics (Accuracy, Recall, Precision, F1, F2, F0.5)