The video tutorial explains the decision tree technique, a supervised machine learning method that splits data based on parameters to form a tree with decision and leaf nodes. It describes the learning algorithm, which involves selecting features and segmenting data based on their importance. The tutorial also introduces criteria like entropy used in decision-making processes.

What is the primary purpose of a decision tree in machine learning?

• To split data into smaller subsets based on parameters

• To combine data into larger sets

• To eliminate data redundancy

• To increase data complexity

What is a decision node in a decision tree?

• A point where data is split into branches

• A point where data is combined

• A point where data is stored

• A point where data is deleted

How does a decision tree algorithm determine the best feature for data segmentation?

• By randomly selecting a feature

• By evaluating the feature's importance

• By choosing the first feature in the dataset

• By selecting the feature with the least data

What is the role of criteria and measurements in decision trees?

• To delete unnecessary data

• To make decisions on data segmentation

• To store data efficiently

• To visualize data

Which criterion is mentioned as a method for making decisions in decision trees?

• Entropy

• Variance

• Standard deviation

• Mean