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pdf('HW2.pdf') ##HW2 (CH3:2,9,10,12) ##Q2. #The KNN classifier is typically used to solve classification problems (those with a qualitative response) by identifying the neighborhood of x0 and then estimating the conditional probability P(Y=j|X=x0) for class j as the fraction of points in the neighborhood whose response values equal j. #The KNN r...
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HW5
#2 #a. #iii because the lasso method is less flexible than the least squares method and hence will give improved prediction accuracy when its increase in bias is less than its decrease in variance #b. #iii because the ridge regression is less flexible than the least squares method and hence will give improved prediction accuracy when its increase...
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Document
pdf(‘HW3.pdf’) ##HW3 (CH4:10,11,13) ##Q10. #a library(ISLR) summary(Weekly) cor(Weekly[, -9]) attach(Weekly) plot(Volume) #The only variables that show a correlation is between Year and Volume #b fit.glm = glm(Direction ~ Lag1 + Lag2 + Lag3 + Lag4 + Lag5 + Volume, data = Weekly, family = binomial) summary(fit.glm) #It would seem that Lag2 is ...
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Homework #
This is an R HTML document. When you click the Knit HTML button a web page will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this: ##Q10. #a library(ISLR) summary(Weekly) ## Year Lag1 Lag2 Lag3 ...
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HW4
title: “Homework 4” author: “Elaine Perera” date: “3/18/2021” output: html_document ## Chapter 05 (page 197): 3, 5, 6, 9 #Q3 #a. #This approach involves randomly 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...
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HW6
Q6. #a library(ISLR) library(boot) set.seed(1) degree = 10 cv.errs = rep(NA, degree) for (i in 1:degree) { fit = glm(wage ~ poly(age, i), data = Wage) cv.errs[i] = cv.glm(Wage, fit)\(delta[1] } plot(1:degree, cv.errs, xlab = 'Degree', ylab = 'Test MSE', type = 'l') deg.min = which.min(cv.errs) points(deg.min, cv.errs[deg.min], col = 'red', cex = ...
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HW6
Chapter 07 (page 297): 6, 10 Question #6 library(ISLR) library(MASS) library(ggplot2) library(data.table) library(leaps) library(glmnet) ## Loading required package: Matrix ## Loaded glmnet 4.1-1 library(boot) set.seed(5) data(Wage) #a deltas = rep(NA, 10) for (i in 1:10) { glm.model = glm(wage~poly(age, i), data=Wage) deltas[i] = cv.glm(Wage...
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HW8
Chapter 09 (page 368): 5, 7, 8 Question#5 #a set.seed(421) x1 = runif(500) - 0.5 x2 = runif(500) - 0.5 y = 1 * (x1^2 - x2^2 > 0) #b plot(x1[y == 0], x2[y == 0], col = "red", xlab = "X1", ylab = "X2", pch = "+") points(x1[y == 1], x2[y == 1], col = "blue", pch = 4) #c lm.fit = glm(y ~ x1 + x2, family = binomial) summary(lm.fit) ## ## Call: ## g...
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HW7
#Chapter 08 (page 332): 3, 8, 9 #Q3 p = seq(0, 1, 0.01) gini.index = 2 * p * (1 - p) class.error = 1 - pmax(p, 1 - p) cross.entropy =- (p * log(p) + (1 - p) * log(1 - p)) matplot(p, cbind(gini.index, class.error, cross.entropy), col = c(“red”, “green”, “blue”)) #Q8 #a library(ISLR) set.seed(1) train = sample(1:nrow(Carseats), nrow(Car...
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HW8
Chapter 09 (page 368): 5, 7, 8 Q5 a set.seed(421) x1 = runif(500) - 0.5 x2 = runif(500) - 0.5 y = 1 * (x1^2 - x2^2 > 0) #b plot(x1[y == 0], x2[y == 0], col = “red”, xlab = “X1”, ylab = “X2”, pch = “+”) points(x1[y == 1], x2[y == 1], col = “blue”, pch = 4) #c lm.fit = glm(y ~ x1 + x2, family = binomial) summary(lm.fit) #d dat...
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