Lab 3: Added files

lab2/assignment1.R

@@ -27,6 +27,7 @@ mse <- function(y, y_hat)
 train_pred <- predict(model, train)
 test_pred <- predict(model, test)
 
+
 print(paste("MSE on the training data:", mse(train$Fat, train_pred)))
 print(paste("MSE on the test data:", mse(test$Fat, test_pred)))
 
@@ -82,8 +83,11 @@ fit <- cv.glmnet(as.matrix(X_train), y_train, alpha = 1)
 plot(fit, xvar = "lambda", label = TRUE)
 grid()
 
+
 fit$lambda.min
 
+
+
 sum(coef(fit, s = "lambda.min")[-1] != 0) # ignore intercept
 
 y_hat = predict(fit, newx = as.matrix(X_test), s = "lambda.min")

lab3/Lab3Block1_2021_SVMs_St.R

+# Lab 3 block 1 of 732A99/TDDE01/732A68 Machine Learning
+# Author: jose.m.pena@liu.se
+# Made for teaching purposes
+
+library(kernlab)
+set.seed(1234567890)
+
+data(spam)
+foo <- sample(nrow(spam))
+spam <- spam[foo,]
+tr <- spam[1:3000, ]
+va <- spam[3001:3800, ]
+trva <- spam[1:3800, ]
+te <- spam[3801:4601, ] 
+
+by <- 0.3
+err_va <- NULL
+for(i in seq(by,5,by)){
+  filter <- ksvm(type~.,data=tr,kernel="rbfdot",kpar=list(sigma=0.05),C=i,scaled=FALSE)
+  mailtype <- predict(filter,va[,-58])
+  t <- table(mailtype,va[,58])
+  err_va <-c(err_va,(t[1,2]+t[2,1])/sum(t))
+}
+
+filter0 <- ksvm(type~.,data=tr,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
+mailtype <- predict(filter0,va[,-58])
+t <- table(mailtype,va[,58])
+err0 <- (t[1,2]+t[2,1])/sum(t)
+err0
+
+filter1 <- ksvm(type~.,data=tr,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
+mailtype <- predict(filter1,te[,-58])
+t <- table(mailtype,te[,58])
+err1 <- (t[1,2]+t[2,1])/sum(t)
+err1
+
+filter2 <- ksvm(type~.,data=trva,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
+mailtype <- predict(filter2,te[,-58])
+t <- table(mailtype,te[,58])
+err2 <- (t[1,2]+t[2,1])/sum(t)
+err2
+
+filter3 <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=which.min(err_va)*by,scaled=FALSE)
+mailtype <- predict(filter3,te[,-58])
+t <- table(mailtype,te[,58])
+err3 <- (t[1,2]+t[2,1])/sum(t)
+err3
+
+# Questions
+
+# 1. Which filter do we return to the user ? filter0, filter1, filter2 or filter3? Why?
+
+# 2. What is the estimate of the generalization error of the filter returned to the user? err0, err1, err2 or err3? Why?
+
+# 3. Implementation of SVM predictions.
+
+sv<-alphaindex(filter3)[[1]]
+co<-coef(filter3)[[1]]
+inte<- - b(filter3)
+k<-NULL
+for(i in 1:10){ # We produce predictions for just the first 10 points in the dataset.
+  k2<-NULL
+  for(j in 1:length(sv)){
+    k2<- # Your code here
+  }
+  k<-c(k, # Your code here)
+}
+k
+predict(filter3,spam[1:10,-58], type = "decision")
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