1.Given the dendrogram obtained from hierarchical clustering of the mtcars dataset, is Masserati Bora more similar to Chrysler Imperial or to Cadillac Fleetwood? Maserati Bora is more similar to the Chrysler Imperial because it is closer on the horizontal axis. Evaluate each statement and indicate whether it is true or false: A. PCA is a supervised learning algorithm used for classification tasks. [ Select ] FALSE TRUE B. PCA transforms the original features into a new set of correlated features called principal components. [ Select ] FALSE TRUE C. The first principal component captures the maximum variance in the data. [ Select ] TRUE FALSE D. If we run PCA multiple times, the results will differ depending on the seed (set.seed()) used. [ Select ] TRUE FALSE Why it is important to set the scale = TRUE when performing PCA? [ Select ] To increase the computational efficiency of the PCA algorithm. To ensure that each feature contributes equally to the analysis by standardizing the data. To automatically select the optimal number of principal components. To ensure that the principal components are orthogonal to each other. model <- prcomp(numeric.dat, scale = TRUE) Given two Kernel Density Estimations (KDE) of the same dataset, one created with a small bandwidth and the other with a larger bandwidth, which of the following statements is more likely to be true? [ Select ] The KDE with a small bandwidth will produce a smoother and more generalized density estimate. The KDE with a larger bandwidth will capture finer details and more peaks in the data distribution. The KDE with a small bandwidth will be more sensitive to noise and show more peaks in the density estimate. The KDE with a larger bandwidth will overfit to the data and show more fluctuations in the density estimate. Match each ggplot graph with the corresponding model it represents Graph 1 A scatter plot for the linear regression model with fitted values and residuals. Graph 2 [ Select ] A biplot for the PCA. A scatter plot for the linear regression model with fitted values and residuals. Given two t-SNE visualizations of the author dataset, one created with a small perplexity and the other with a large, which of the statement is more likely to be true? [ Select ] Graph 2 is more likely to be created by a small perplexity as it focues more on local structures and distinct clusters. Graph 1 is more likely to be created by a large perplexity as it shows finer details. Graph 1 is more likely to be created by a small perplexity as it is more stable and reproducible. Graph 2 is more likely to be created by a large perplexity as it emphasises global data structure. plot of chunk tsne