Quiz on Machine Learning: Clustering and k-NN Algorithm

What is the primary goal of classification in machine learning?

• To visualize data in 3D

• To reduce the dimensionality of data

• To assign data points to specific categories

• To gather unlabelled data

Which of the following is NOT a common classification algorithm?

• Decision Trees

• Naive Bayes

• Support Vector Machines

• K-Means

What does the term 'features' refer to in classification?

• The distinct categories of data

• The attributes used to classify data points

• The output labels of the data

• The training dataset size

In K-NN, what does the 'K' represent?

• The number of classes

• The number of nearest neighbors

• The number of features

• The number of training instances

Which distance metric is most commonly used in K-NN?

• Hamming Distance

• Euclidean Distance

• Cosine Similarity

• Manhattan Distance

What is a disadvantage of the K-NN algorithm?

• It is sensitive to noisy data

• It is non-parametric

• It is easy to implement

• It can be used for regression tasks

What is the purpose of a confusion matrix?

• To calculate the distance between data points

• To represent the outcome of a classifier

• To determine the number of features

• To visualize the training data

Which of the following is a performance metric for classification models?

• Distance Metric

• Feature Extraction

• Accuracy

• Distance

What does the F1 score measure?

• The total number of instances

• The number of true positives

• The balance between precision and recall

• The accuracy of the model

In binary classification, how many classes are predicted?

• Multiple

• Three

• Two

• One