Basics of Decision Trees and KNN

Can decision trees handle both categorical and numerical data?

• Yes, but only for small datasets.

• No, decision trees can only handle numerical data.

• No, decision trees cannot handle any type of data.

• Yes, decision trees can handle both categorical and numerical data.

What does KNN stand for?

• K-Nearest Neighbors Algorithm

• K-Nearest Nodes

• K-Nearest Neighbors

• K-Nearest Network

How does K-Nearest Neighbor classify data points?

• KNN classifies data points by random selection.

• KNN classifies data points based on the majority class of their nearest neighbors.

• KNN assigns data points based on the furthest neighbor.

• KNN uses a weighted average of all data points.

In KNN, what does 'K' represent?

• The total number of data points in the dataset.

• The number of nearest neighbors to consider.

• The distance metric used for classification.

• The algorithm used to calculate the average of neighbors.

What type of algorithm is a decision tree?

• Supervised learning algorithm

• Unsupervised learning algorithm

• Clustering algorithm

• Reinforcement learning algorithm

How does a decision tree make decisions?

• A decision tree makes decisions by using a single feature to classify all data points.

• A decision tree makes decisions by randomly selecting features without any criteria.

• A decision tree makes decisions by recursively splitting data based on feature values until it reaches a leaf node.

• A decision tree makes decisions by averaging all feature values to find a midpoint.

What is a leaf node in a decision tree?

• A node with only one child

• A node with two or more children

• A node that represents a decision or classification

• A node that connects two branches of the tree

What does the maximum depth of a decision tree represent?

• • The total number of nodes in the tree

• The length of the longest path from the root to a leaf

• The average number of branches per node

• The total number of leaves in the tree