Question

2. Learning algorithms can be classified as supervised or unsupervised

a. Describe the difference between supervised and unsupervised learning.

b. Contrast k-means clustering with hierarchical clustering outlining similarities and differences.

c. Explain why the iris dataset is popular for testing supervised learning algorithms.

d. Discuss the use of training and testing subsets for supervised learning.

e. What is an R dataframe and what is its role in supervised learning.

Answer 1

(a) In machine learning terms, descriptive data mining is known as unsupervised learning, whereas predictive data mining in known as supervised learning. In improvised learning, we study relationships between the input and output variables; in supervised learning, we explore particular characteristics of the input variables only, such as estimating the point probability density, searching out clusters, drawing proximity maps, locating outliers or imputing missing data. (b) Iris dataset is popular for