Publications by Adrianne Kristianto
Data Algorithms II - Assignment 5
Question 2 For parts (a) through (c), indicate which of i. through iv. is correct. Justify your answer. 2A: The lasso, relative to least squares, is: i. More flexible and hence will give improved prediction accuracy when its increase in bias is less than its decrease in variance. ii. More flexible and hence will give improved prediction accur...
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Data Algorithms II - Assignment 3
Question 10: This question should be answered using the Weekly data set, which is part of the ISLR package. This data is similar in nature to theSmarket data from this chapter’s lab, except that it contains 1, 089 weekly returns for 21 years, from the beginning of 1990 to the end of 2010. 10A: Produce some numerical and graphical summaries of ...
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Data Algorithms II - Assignment 4
Question 3 We now review k-fold cross-validation 3A: Explain how k-fold cross-validation is implemented This approach involves randomly k-fold CV dividing the set of observations into k groups, or folds, of approximately equal size. The first fold is treated as a validation set, and the method is fit on the remaining k − 1 folds. 3B: What ar...
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Data Algorithms II - Assignment 7 (Tree Based Methods)
Question 3 Consider the Gini index, classification error, and entropy in a simple classification setting with two classes. Create a single plot that displays each of these quantities as a function of ˆpm1. The xaxis should display ˆpm1, ranging from 0 to 1, and the y-axis should display the value of the Gini index, classification error, and en...
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Data Algorithms II - Assignment 6 (Nonlinear Models and Tree-based Methods)
Question 6 In this exercise, you will further analyze the Wage data set considered throughout this chapter. a: Perform polynomial regression to predict wage using age. Use cross-validation to select the optimal degree d for the polynomial. What degree was chosen, and how does this compare to the results of hypothesis testing using ANOVA? Make a ...
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Data Algorithms II - Assignment 8 (Support Vector Machines)
Question 5 We have seen that we can fit an SVM with a non-linear kernel in order to perform classification using a non-linear decision boundary. We will now see that we can also obtain a non-linear decision boundary by performing logistic regression using non-linear transformations of the features. a: Generate a data set with n = 500 and p = 2, ...
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Time Series Analysis II
Exercise 1 Use the Electricity.RData examined in HW1 Exercise 2 (d)-(f). Fit the model using first order differenced log transformed series. a: By visually checking, decide what SARIMA models seem appropriate, i.e., specify p, d, q and P, D, Q in SARIMA model, ARIMA(p, d, q) ∗ ARIMA(P,D, Q)s. Choose the most appropriate two models and explain ...
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