Overview

Teaching: 20 min
Exercises: 0 min

Questions

• How to train a Machine Learning model with categorical output

Objectives

• Learn to use different Regression algorithm for Machine Learning training

6 Supervised Learning training with Categorical output

Here we use the R sampled data named iris

data(iris)

6.1 Pre-processing data and treat missing value

Check missing value

sum(is.na(iris))

There are no missing value so we go ahead with visualize the important data

6.2 Visualize the important data

library(GGally)
ggpairs(iris,aes(colour=Species))

6.3 Split data into training and testing

indT <- createDataPartition(y=iris$Species,p=0.6,list=FALSE)
training <- iris[indT,]
testing  <- iris[-indT,]

Let’s use all inputs data for modeling

6.4 Train model and predict with different algorithm

6.4.1 Train model using Linear Discriminant Analyst:

ModFit_lda <- train(Species~.,data=training,
                 preProcess=c("center","scale"),
                 method="lda")
predict_lda <- predict(ModFit_lda,testing)                 

6.4.2 Train model using Naive Bayes

  ModFit_nb <- train(Species~.,data=training,method="nb")
  predict_nb <- predict(ModFit_nb,testing)                 

6.4.3 Train model using Gradient Boosting Machine

ModFit_GBM <- train(Species~.,data=training,method="gbm",verbose=FALSE)
predict_GBM <- predict(ModFit_GBM,testing)

6.4.4 Train model using Random Forest

ModFit_rf <- train(Species~.,data=training,method="rf",prox=TRUE)
predict_rf <- predict(ModFit_rf,testing)                                                            

6.4.5 Train model using Artificial Neural Network

ModNN <- neuralnet(Species~.,training, hidden=c(4,3),linear.output = FALSE)
plot(ModNN)

predict_ann <- compute(ModNN,testing)
# Rescale to original:
yann=data.frame("yhat"=ifelse(max.col(predict_ann$net.result)==1, "setosa",
                              ifelse(max.col(predict_ann$net.result)==2, "versicolor", "virginica")))      

6.5 Evaluate model output

For continuous, we use postResample:

confusionMatrix(predict_lda,testing$Species)
confusionMatrix(predict_nb,testing$Species)
confusionMatrix(predict_GBM,testing$Species)
confusionMatrix(predict_rf,testing$Species)
confusionMatrix(yann$yhat,testing$Species)

Key Points

• categorical output