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5. A is an imaginary or real location denoting the center of the cluster.
6. Groups of similar items are called .
7. In the linear regression equation y = mx+b, m represents .
8. 1 is a perfect positive correlation, is a perfect negative correlation, is no correlation.
9. measures the strength or degree of relationship between two variables.
10. The data type of the two variables in linear regression should be .
C. State whether these statements are true or false.
1. The independent variable is the variable under study, and it is the variable that the regression model
tries to predict.
2. Linear Regression has its limitations, but its simplicity, interpretability, and efficiency often exceed
these limitations.
3. Outliers do not affect the clustering process.
4. A scatterplot is actually a graph of a curvilinear function.
5. If a regression line that was calculated by least squares method is plotted on a scatterplot,
all the points in the dataset should be on the line.
6. Centroid-based clustering arranges the data into hierarchical clusters.
7. Density-based clustering groups high density areas into clusters.
8. Hierarchical clustering builds a tree of clusters.
9. Recommendation systems are widely used by Amazon, Netflix, Flipkart, etc. to provide automated
and personalised recommendations for products, services and information.
10. Email spam filter uses unsupervised learning algorithm.
SECTION B (Subjective Type Questions)
A. Short answer type questions:
1. List 2 advantages of KNN algorithm.
2. Explain the conceptual difference between supervised learning and unsupervised learning.
3. What is Reinforcement learning?
4. What is line of least square errors?
5. What are the advantages and disadvantages of Linear Regression?
6. Justify “Correlation is not Causation”.
7. Interpret the values of correlation coefficient when r=1, -1 and 0.
8. Differentiate between classification and clustering graphically.
B. Long answer type questions:
1. Explain Classification process in detail.
2. Define Linear Regression and list its applications.
3. State four assumption for Pearson’s correlation.
4. Differentiate between Binary and Multiclass Classification.
5. List the steps for K-means clustering.
362 Touchpad Artificial Intelligence (Ver. 3.0)-XI

