Practical Data Science using Python - Decision Tree - Learning Steps

The video tutorial explains decision trees, focusing on their structure, features, and advantages. It covers the process of building decision trees, including selecting the best attributes and conditions using metrics like Gini impurity and entropy. Key concepts such as nodes, splitting, and pruning are discussed, highlighting how decision trees handle data and avoid overfitting. The tutorial also emphasizes the simplicity and interpretability of decision trees, making them a valuable tool in data analysis.

What is the primary purpose of a decision tree?

• To model relationships using a matrix

• To model relationships using a linear equation

• To model relationships using a tree of rules

• To model relationships using a circular graph

What is the main challenge in building a decision tree?

• Finding the best color for the tree

• Finding the best attribute to split the data

• Finding the best time to run the algorithm

• Finding the best software to visualize the tree

Which of the following is NOT an advantage of decision trees?

• They are difficult to interpret

• They require little data preparation

• They work well with categorical data

• They can handle multi-output problems

What is a leaf node in a decision tree?

• A node that has no children

• A node that contains the final decision or label

• A node that is at the top of the tree

• A node that splits data into two parts

What is the purpose of pruning in decision trees?

• To add more branches to the tree

• To prevent overfitting by reducing the tree's complexity

• To increase the tree's height

• To change the tree's color

What happens when a decision tree overfits?

• It becomes faster at making predictions

• It becomes more colorful

• It becomes too complex and fits the training data too closely

• It becomes too simple and loses accuracy

What is the role of the root node in a decision tree?

• It is the final decision node

• It is the first condition or decision point in the tree

• It is the node with the most branches

• It is the node with the least importance

What are the two metrics used to evaluate splits in decision trees?

• Accuracy and precision

• Mean squared error and R-squared

• Recall and F1 score

• Gini impurity and entropy

What is the goal of a decision tree when splitting data?

• To achieve the most colorful form of data

• To achieve the purest form of data

• To achieve the most random form of data

• To achieve the most complex form of data

How does a decision tree determine the best feature to split on?

• By choosing the feature with the highest Gini index

• By choosing the feature with the lowest Gini index or entropy score

• By choosing the feature with the most colors

• By choosing the feature with the longest name