Decision Trees Quiz

What is the main goal of a Decision Tree in classification?

• To create a set of rules for data clustering

• To find patterns in unlabeled data

• To split the dataset into branches that lead to class labels

• To calculate the mean and variance of data

What is the first step in building a Decision Tree?

• Selecting the best attribute to split the data

• Pruning the tree to reduce overfitting

• Assigning class labels to each data point

• Randomly selecting data points for classification

Which criterion is commonly used to split nodes in a Decision Tree?

• Mean Squared Error (MSE)

• Gini Index or Information Gain

• Cosine Similarity

• Jaccard Index

What does "pruning" do in Decision Trees?

• Adds more branches to the tree

• Removes unnecessary branches to prevent overfitting

• Changes the decision tree into a random forest

• Increases the number of features used for classification

In Decision Trees, what does a "leaf node" represent?

• A decision point where data splits

• A class label or final outcome

• A feature selection point

• A missing value in the dataset

What is the major drawback of Decision Trees?

• They require labeled training data

• They are prone to overfitting on complex datasets

• They cannot handle numerical data

• They only work for regression problems

How can overfitting in Decision Trees be reduced?

• By using pruning techniques

• By reducing the number of training examples

• By increasing the depth of the tree

• By ignoring irrelevant attributes

What is a Random Forest in relation to Decision Trees?

• A single deep decision tree

• A collection of multiple decision trees working together

• A tree where all nodes are chosen randomly

• A decision tree with a single feature

Which of the following algorithms is used to construct Decision Trees?

• K-Means Clustering

• Apriori Algorithm

• ID3, C4.5, or CART

• Support Vector Machines

How does the Gini Index work in Decision Trees?

• It measures the probability of a randomly chosen element being correctly classified

• It calculates the distance between data points

• It finds the average depth of the tree

• It checks how many branches are needed in the tree