Training Machine Learning model using Model based Prediction

Overview

Teaching: 20 min
Exercises: 0 min

Questions

• What is model based prediction algorithm in ML?

Objectives

• Learn to use different Model based prediction for Machine Learning training

8.1 Naive Bayes

• Assuming data follow a probabilistic model
• Assuming all predictors are independent (Naïve assumption)
• Use Bayes’s theorem to identify optimal classifiers

8.1 Implementation Naive Bayes

ModFit_NB <- train(Species~., data=training, method="nb")

predict_NB <- predict(ModFit_NB,testing)
confusionMatrix(testing$Species,predict_NB)

8.2 Linear Discriminent Analysis

• LDA is a supervised learning model that is similar to logistic regression in that the outcome variable is categorical and can therefore be used for classification.
• LDA is useful with two or more class of objects

8.2.1 Implementation LDA

ModFit_LDA <- train(Species~., data=training, method="lda")

predict_LDA <- predict(ModFit_LDA,testing)
confusionMatrix(testing$Species,predict_LDA)

• Ensemble approach (Bagging) with LDA

ModFit_ldabag <- train(training[,-5],training$Species,method="bag",B=500,
                       bagControl=bagControl(fit=ldaBag$fit,
                                             predict=ldaBag$pred,
                                             aggregate = ldaBag$aggregate))

predict_bag <- predict(ModFit_ldabag,testing)
confusionMatrix(predict_bag, testing$Species)

Key Points

• Naive Bayes, Linear Discriminent Analyst