Publications by Vishalakshi Arumugam
Moving Beyond Linearity- Assignment 6
Question 6 In this exercise , further analyze the wage dataset considered throughout the chapter data("Wage") str(Wage) ## 'data.frame': 3000 obs. of 11 variables: ## $ year : int 2006 2004 2003 2003 2005 2008 2009 2008 2006 2004 ... ## $ age : int 18 24 45 43 50 54 44 30 41 52 ... ## $ maritl : Factor w/ 5 levels "1. Never...
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Algorithm II-Assignment 5
Question 2 For part (a) through (c), indicate which of i. through iv. is correct. Justify your answer. Question 2a The Lasso regression relative to least squares is Less flexible and hence will get improved prediction accuracy when its increase in bias is less than its decrease in variance.At Least Squares coefficient estimates , the variance is...
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Algorithms II- Assignment 2
Question 2 Carefully explain the differences between KNN classifier and KNN regression methods KNN Classifier KNN Regression 1) It is mainly used to predict the qualitative response It is mainly used to predict the quantitative response 2) Prediction of response is based on the highest estimated probability of the specified K number of nearest...
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Algorithm II-Classification Assignment
Question 10 This question should be answered using the weekly data set which is a part of ISLR package and it contains 1089 weekly returns from the beginning of 1990 to the end of 2010 Question 10(a) Produce some numerical and graphical summaries of the weekly data. Do there appear to be any patterns names(Weekly) ## [1] "Year" "Lag1" ...
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Algorithm II- Resampling Methods Assignment
Question 3 Question 3a Explain how K-fold Cross validation is implemented Steps For K-fold Crossvalidation Dataset is divided in to k number of groups(folds) The first fold is held out as validation fold. Model is trained with Remaining K- number of folds which in turn used to predict the outcome for the observations from validation fold. The ...
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Assignment 7- Tree Based Methods
Question 3 Consider the Gini Index, Classification error and entrophy in a simple classification setting with two classes. Create a single plot that displays each of these quantities as a function of \(\hat{p}_{m1}\). The x-axis should display \(\hat{p}_{m1}\), ranging from 0 to 1 and the y-axis should display the value of Gini index, classificat...
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Support vector machines-Assignment8
Question 5 We can seen that we can fit an SVM with a non linear kernel inorder 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 feature Question 5a Generate a data set with n=500 and p=2,...
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