Hyperparameters Optimization Strategies: GridSearch, Bayesian, & Random Search (Beginner Friendly!)

In this video, we will cover key hyperparameters optimization strategies such as: Grid search, Bayesian, and Random Search.

Hyperparameter optimization is a key step in developing any machine learning project. After training multiple models, you would like to fine tune them so that they perform better on a given dataset.

1. Grid Search:
Performs exhaustive search over a specified list of parameters.
You provide the algorithm with the hyperparameters you’d like to experiment with and the values we want to try out.

2. Randomized Search:
Grid search works great if the number of combinations are limited.
In scenarios when the search space is large, RandomizedSearchCV is preferred.

The algorithm works by evaluating a select few numbers of random combinations. You have the freedom and control over the number of iterations.

3. Bayesian Optimization:
Bayesian optimization overcomes the drawbacks of random search algorithms by exploring search spaces in a more efficient manner.

If a region in the search space appears to be promising (i.e.: resulted in a small error), this region should be explored more which increases the chances of achieving better performance! You will need to specify the parameters search space.


I hope you will enjoy this video and find it useful and informative.

Thanks and Happy Learning!

#Hyperparameterstuning #optimizationtechniques