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.

What is the primary difference between Gini impurity and entropy in decision trees?

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What are some advantages of using decision trees?

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Explain why decision trees do not require input data to be normalized.

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What types of data can decision trees handle?

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Describe the concept of overfitting in decision trees.

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Discuss the impact of high variance in decision trees.

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What is the significance of the Gini index in decision trees?

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How can hyperparameters help in controlling the growth of decision trees?

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What is the role of the 'min sample split' hyperparameter in decision trees?

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How does the 'max leaf nodes' parameter affect a decision tree?

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