Iris Data manipulation desk

Niranjan Agnihotri
3rd August, 2017

Purpose

This app is designed for the user to subset the iris data set and train and evaluate different models and check the results to evaluate different models.

Select the train

In the numeric input box, enter the percentage of the data set to be considered as training set. Press ok. The data sets will be split likewise showing the number of rows for the. test and train sets in the main panel. As follows -

Data sets

library(caret)
head(iris, 2)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa

Test and train sets

inTrain <- createDataPartition(iris$Species, p=.7, list=FALSE)
train <- iris[inTrain,]
test <- iris[-inTrain,]
print(dim(train))

[1] 105   5

print(dim(test))

[1] 45  5

Select the model

From the drop down list select the model for which you have to train the data set. Click on 'Train and Predict' button to train the model and check the evaluation of the model on the test set.

Thank you