Скачать или смотреть 24. Hyperparameter Tuning with GridSearchCV – Find the Best Model Parameters! 🚀

Welcome back to the Machine Learning Classification series!

In this video, we’ll dive into Hyperparameter Tuning using GridSearchCV, a systematic approach to finding the best parameters for your machine learning model. Fine-tuning your model can boost performance and ensure optimal predictions!

What You’ll Learn:
✅ What is Hyperparameter Tuning, and why is it important?
✅ Difference between GridSearchCV and RandomizedSearchCV
✅ How to use GridSearchCV in Scikit-Learn
✅ Optimizing key parameters for RandomForestClassifier, Logistic Regression, and more
✅ Evaluating and selecting the best model configuration

By the end of this video, you'll be able to systematically tune hyperparameters to improve accuracy and generalization.

📌 Useful Links
📊 Dataset Links:
📌 Heart Disease Dataset 1: https://www.kaggle.com/datasets/johns...
📌 Heart Disease Dataset 2: https://www.kaggle.com/datasets/deeks...

🗺️ Machine Learning Workflow Map:
📌 Scikit-Learn ML Map: https://scikit-learn.org/stable/machi...

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#MachineLearning #HyperparameterTuning #GridSearchCV #DataScience #MLforBeginners #LearnMachineLearning #ScikitLearn #PythonProgramming #AI #DataAnalytics #ModelOptimization #CodingTutorial