Practical Data Science using Python - Decision Tree - Hyperparameter Tuning

The video tutorial covers decision trees, focusing on Gini impurity and entropy as measures for splitting nodes. It highlights the advantages of decision trees, such as interpretability and handling categorical data without scaling. The tutorial also addresses disadvantages like overfitting and instability. It explains hyperparameters for regularization to prevent overfitting and concludes with a practical example using the Iris dataset to demonstrate decision tree application.

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