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 a key difference between Gini impurity and entropy in decision trees?

• Gini impurity is slower to compute than entropy.

• Entropy tends to isolate the most frequent class.

• Gini impurity often results in more balanced trees.

• Entropy tends to produce more balanced trees.

Why are decision trees considered highly interpretable?

• They assume linear relationships.

• They need categorical data transformation.

• They have a graphical representation.

• They require data scaling.

Which of the following is NOT a requirement for decision trees?

• All of the above

• Stringent assumptions on input data

• Categorical data transformation

• Data normalization

What is a major disadvantage of decision trees?

• They are difficult to interpret.

• They cannot handle categorical data.

• They tend to overfit the data.

• They require data scaling.

How can overfitting in decision trees be controlled?

• By increasing the number of features

• By using linear regression

• By using hyperparameters

• By normalizing the data

What does the 'min_samples_split' hyperparameter control?

• The maximum number of leaf nodes

• The minimum number of samples required to split a node

• The number of features to consider for a split

• The maximum depth of the tree

Which hyperparameter limits the number of leaf nodes in a decision tree?

• min_samples_split

• max_leaf_nodes

• max_depth

• min_samples_leaf

What is the purpose of the 'max_depth' hyperparameter?

• To ensure data normalization

• To specify the minimum samples in a leaf

• To control the maximum depth of the tree

• To limit the number of features evaluated

In the Iris dataset example, what is the primary goal of using a decision tree?

• To increase the number of features

• To classify observations into different species

• To normalize the data

• To transform categorical data

Which classes are included in the Iris dataset used in the example?

• Iris virginica, Iris rosea, Iris alba

• Iris setosa, Iris versicolor, Iris rosea

• Iris setosa, Iris versicolor, Iris virginica

• Iris versicolor, Iris rosea, Iris alba