Gaussian Discriminant Function
Also known as Gaussian Naive Bayes, is a popular technique in machine learning and pattern recognition for classification problems. It is a type of generative model that makes assumptions about the distribution of the predictor variables in each class. Specifically, it assumes that the predictor variables are normally distributed within each class and that the covariance matrices of the predictor variables are equal across all classes.
Here’s how you can apply Naive Bayes classification to the Iris dataset in R.
- Load the Iris dataset.
- Split the dataset into training and testing sets.
- Fit the Naive Bayes model using the naiveBayes() function from the e1071 package. The formula Species ~. specifies that we want to predict the species based on all of the other variables in the dataset.
- Use the predict() function to make predictions on the test set.
- The predict() function returns a vector of predicted class labels.
- Evaluate the performance of the model using the confusion matrix.
R
library(MASS) library(caret) data(iris) set.seed(123) trainIndex <- createDataPartition(iris$Species, p = 0.8, list = FALSE) train <- iris[trainIndex, ] test <- iris[-trainIndex, ] # Fit a GDA model library(e1071) gda_model <- naiveBayes(Species ~ ., data = train) predicted <- predict(gda_model, newdata = test) confusionMatrix(predicted, test$Species) |
Output:
Analysis report by using Gaussian Discriminant Analysis
Discriminant Function Analysis Using R
Discriminant Function Analysis (DFA) is a statistical method used to find a discriminant function that can separate two or more groups based on their independent variables. In other words, DFA is used to determine which variables contribute most to group separation. DFA is a helpful tool in various fields such as finance, biology, marketing, etc.
In this article, we will discuss the concepts related to DFA, the steps needed to perform DFA, provide good examples, and show the output of the analysis using the R programming language.