validation_recc.R

#Evaluate Model M0

actual1<-data_test
pred1<-popularity_rec

# create the confusion matrix
cm = as.matrix(table(Actual = actual1, Predicted = pred1)) 
cm


n = sum(cm) # number of instances
nc = nrow(cm) # number of classes
diag = diag(cm) # number of correctly classified instances per class 
rowsums = apply(cm, 1, sum) # number of instances per class
colsums = apply(cm, 2, sum) # number of predictions per class
p = rowsums / n # distribution of instances over the actual classes
q = colsums / n # distribution of instances over the predicted classes

precision = diag / colsums 
recall = diag / rowsums 
df<-data.frame(precision, recall) 



#Evaluate Model M1

actual1<-data_train
pred2<-reccom

# create the confusion matrix
cm1 = as.matrix(table(Actual = actual1, Predicted = pred2)) 
cm1


n1 = sum(cm1) # number of instances
nc1 = nrow(cm1) # number of classes
diag1 = diag(cm1) # number of correctly classified instances per class 
rowsums1 = apply(cm1, 1, sum) # number of instances per class
colsums1 = apply(cm1, 2, sum) # number of predictions per class
p = rowsums1 / n1 # distribution of instances over the actual classes
q = colsums1 / n1 # distribution of instances over the predicted classes

precision = diag1 / colsums1 
recall = diag1 / rowsums1 
df1<-data.frame(precision, recall)