MLTT UNIT 2 TEST

Which of the following is NOT an appropriate problem for Decision Tree learning?

• Classification

• Regression

• Clustering

• Multi-class prediction

What criterion is commonly used to select the attribute that best splits the data at each node?

• Information Gain

• Euclidean Distance

• Manhattan Distance

• k-Means Clustering

In Decision Trees, what does each internal node represent?

• A class label

• A feature test

• A sample instance

• A probability distribution

Which of these is a common issue in Decision Tree learning?

• Overfitting

• Underfitting

• High bias

• All of the above

The inductive bias of a Decision Tree learner assumes that:

• Shorter trees are preferred over longer trees

• Deeper trees always generalize better

• Trees with more splits are better

• Features are irrelevant

Which Python library is most commonly used to implement Decision Trees?

• seaborn

• scikit-learn

• matplotlib

• nltk

In K-Nearest Neighbors, what is 'k'?

• Number of clusters

• Number of neighbors considered

• Number of features

• Depth of the tree

What problem arises when the number of features is very large in KNN?

• Overfitting

• Curse of Dimensionality

• Underfitting

• Bias Variance Trade-off

Which of these is a feature selection method that adds features iteratively?

• Backward Search

• Forward Search

• Principal Component Analysis

• Information Gain

Which feature reduction technique transforms features into a new set of uncorrelated variables?

• Univariate Selection

• Multivariate Selection

• Principal Component Analysis

• Forward Selection