Hyperparameter Tuning in Machine Learning Model using Optuna

Описание: In this video, you’ll learn how to perform hyperparameter optimization using Optuna—a powerful open-source Python framework designed for automating the search for optimal parameters in machine learning models.

This hands-on tutorial walks you through every step of using Optuna to tune a Random Forest Classifier on the classic Iris dataset, covering both the practical coding aspects and the underlying concepts.

What You’ll Learn:
1.Introduction to Optuna
Get to know what Optuna is, its key features, and why it’s one of the most efficient tools for hyperparameter optimization.

2.Dataset Preparation
Learn how to import essential libraries, load the Iris dataset, and split the data for training and testing.

3.Defining the Objective Function
Understand how to create an objective function that tells Optuna how to evaluate different combinations of hyperparameters for the Random Forest model.

4.Running the Optuna Optimization
Step-by-step guidance on setting up an Optuna study, configuring the number of trials, and running the optimization process.

5.Retrieving and Interpreting Results
See how to access the best hyperparameters found by Optuna and understand the evaluation metrics and output details.

6.Adaptability Tips
Suggestions for modifying the code to work with other datasets, models, or additional hyperparameters.

Who is this video for?
-Aspiring data scientists and machine learning practitioners interested in efficient model tuning
-Anyone looking to automate and streamline their machine learning workflow
-Beginners who want a clear, practical introduction to Optuna in Python

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