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Regularization in Decision Trees | Hyperparameters Explained 🌳🔧

In this video, we explore how regularization hyperparameters help control the complexity of Decision Tree classifiers and prevent overfitting.

📌 What You’ll Learn:

What is overfitting in decision trees?

Key regularization hyperparameters:

max_depth

min_samples_split

min_samples_leaf

max_leaf_nodes

max_features

How these parameters influence tree growth and model performance

Practical examples using Scikit-learn (sklearn)

Tips for tuning hyperparameters using GridSearchCV

Whether you're a beginner in machine learning or refining your models for production, understanding these regularization techniques is essential to building robust and generalizable models.

💡 Ideal for: ML students, data scientists, and developers looking to enhance their model tuning skills.

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📚 Resources & Code Examples:
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