Practical Machine Learning with R and Python: Machine Learning in Stereo by Tinniam V Ganesh

Author:Tinniam V Ganesh [Ganesh, Tinniam V]
Language: eng
Format: azw3, pdf
Published: 2017-12-01T05:00:00+00:00

# Fit a model for 1,3,5,10 and 15 neighbors

cMat <- NULL

neighbors <-c(1,3,5,10,15)

for(i in seq_along(neighbors)){

fit =knn(train.X,test.X,train.y,k=i)

table(fit,test.y)

a<-confusionMatrix(fit,test.y)

cMat[i] <- a$overall[1]

print(a$overall[1])

}

## Accuracy

## 0.7835831

## Accuracy

## 0.8162047

## Accuracy

## 0.8089113

## Accuracy

## 0.8209787

## Accuracy

## 0.8184591

#Plot the Accuracy for each of the KNN models

df <- data.frame(neighbors,Accuracy=cMat)

ggplot(df,aes(x=neighbors,y=Accuracy)) + geom_point() +geom_line(color="blue") +

xlab("Number of neighbors") + ylab("Accuracy") +

ggtitle("KNN regression - Accuracy vs Number of Neighors (Unnormalized)")

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