Iris Classifier Evaluation

You are given the Iris dataset and your task is to implement two classifiers:

• A Decision Tree Classifier
• A Random Forest Classifier

Using scikit-learn, you will split the data into training and testing sets with a fixed random state. Train the classifiers then compute the accuracy and confusion matrix for both models. The decision tree should produce an accuracy \(a_{DT}\) and the random forest \(a_{RF}\) accuracy such that \(a_{DT} > 0.9\) and \(a_{RF} > 0.9\).

After evaluation, output 8 lines to stdout in the following order:

1. The decision tree accuracy rounded to 4 decimals.
2. The random forest accuracy rounded to 4 decimals.
3. Three lines for the confusion matrix of the decision tree (each row as space‐separated integers).
4. Three lines for the confusion matrix of the random forest.

Note: Any formulas must be displayed in LaTeX format (e.g. \(\cdot\)).

inputFormat

No input from STDIN is required.

outputFormat

The program should print exactly 8 lines to STDOUT:

1. Decision Tree accuracy (rounded to 4 decimal places).
2. Random Forest accuracy (rounded to 4 decimal places).
3. 3 lines representing the decision tree’s confusion matrix (each line contains 3 space‐separated integers).
4. 3 lines representing the random forest’s confusion matrix (each line contains 3 space‐separated integers).
## sample

1.0000
0.9778
16 0 0
0 13 0
0 0 16
16 0 0
0 13 0
0 1 15

</p>