1. High entropy means that the partitions in classification are :
a) pure
b) not pure
c) useful
d) useless
2. A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?
a) 12
b) 24
c) 48
d) 72
3. Which of the following is NOT supervised learning?
a) PCA
b) Decision Tree
c) Linear Regression
d) Naive Bayesian
4. Which of the following statements about Naive Bayes is incorrect?
a) Attributes are equally important.
b) Attributes are statistically dependent of one another given the class value.
c) Attributes are statistically independent of one another given the class value.
d) Attributes can be nominal or numeric
5. Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate?
a) Decision Trees
b) Density-based clustering
c) Model-based clustering
d) K-means clustering
Note :- The answer will be going to be posted on Wednesday.
Try to answer the question and learn something new everyday.
Looking forward for your participation. 
