Decision Tree from Scratch to Tuned Model | Machine Learning Practical
Description In this video, we perform a practical implementation of Decision Tree Classifier in Python and improve its performance using hyperparameter tuning with GridSearchCV. You’ll see how tuning parameters like max_depth, min_samples_split, min_samples_leaf, and criterion helps reduce overfitting and increases model accuracy. 🔍 What you will learn in this video: Dataset loading and preprocessing Decision Tree Classifier implementation Model training and testing Understanding Decision Tree hyperparameters Hyperparameter tuning using GridSearchCV Best parameter selection Model evaluation (Accuracy, Confusion Matrix, Classification Report) This tutorial is ideal for students, beginners, and Data Science learners. 📌 Tools & Libraries Used: Python Scikit-learn NumPy Pandas Matplotlib / Seaborn 👍 Like, Share & Subscribe for more Machine Learning practical tutorials.
