Decision Tree Learning Quiz

What is the primary goal of decision tree learning?

• To cluster similar data points

• To generate if-then rules for classification or regression

• To reduce data dimensionality

• To perform reinforcement learning

Which algorithm is known for using Information Gain as its attribute selection measure?

• CART

• C4.5

• ID3

• k-NN

What is the main purpose of pruning a decision tree?

• To increase the depth of the tree

• To eliminate redundant features

• To reduce overfitting and improve generalization

• To improve computational speed during training

Which measure is used in C4.5 to mitigate the bias of Information Gain toward attributes with many values?

• Gini Index

• Gain Ratio

• Chi-Square Statistic

• Relief

What does the Split Information measure indicate?

• The purity of the node after the split

• How many examples are misclassified after the split

• How broadly and uniformly an attribute splits the data

• The computational cost of splitting on an attribute

Overfitting in a decision tree occurs when:

• The tree is too shallow and underfits the data

• The tree perfectly classifies the training data but generalizes poorly

• The tree uses only a single attribute

• The tree has high bias and low variance

Which method is commonly used to handle continuous-valued attributes in decision trees?

• One-hot encoding

• Threshold-based splitting

• Principal Component Analysis (PCA)

• k-means clustering

In a decision tree, a leaf node represents:

• A decision rule based on multiple attributes

• A split that further divides the dataset

• A final classification or regression value

• An internal node with missing values

One disadvantage of decision trees is that they are often:

• Too robust to noisy data

• Uninterpretable due to their simplicity

• Highly sensitive to small changes in data (high variance)

• Inefficient in terms of computation

In Bayes' theorem, what does the term 𝑃(𝐷∣ℎ) P(D∣h) represent?

• The prior probability of the hypothesis

• The likelihood of the data given the hypothesis

• The probability of the hypothesis given the data

• The evidence for the data