Decision Tree Classifier on Iris Dataset

In this problem, you are tasked with implementing a Decision Tree Classifier for the Iris dataset. You must handle any missing values using mean imputation and standardize the dataset before training the classifier. The dataset should be divided into 80% training and 20% testing sets using a fixed random seed to ensure reproducibility. Finally, you are required to compute and print the accuracy of your classifier on the testing set as a floating-point number in LaTeX format rounded to five decimal places (i.e., (accuracy = 0.96667)).

inputFormat

There is no input for this problem. You should ignore any data received through STDIN.

outputFormat

Output a single floating-point number representing the accuracy of the trained Decision Tree Classifier, formatted to five decimal places.## sample

0.96667