This is the complete question

In this question, you will experiment with different classification models on the Iris data set. Split the data set into a stratified training and test set (train test split is a helpful sklearn utility for this task).

The models are:

• Ridge Classifier
• Logistic Regression

(a) For the Ridge Classifier, use the default parameters, and report the overall accuracy and confusion matrix, on the test set. Repeat five times for five different splits of the data set, and compute the mean and standard deviation of accuracy over the five runs.

(b) For the Logistic Regression classifier, repeat part (a)

(c) Summarize your findings from (a) and (b). What is the best performing classifier on the iris data set, considering the mean and standard deviation of accuracy of the two classifiers?

https://archive.ics.uci.edu/ml/machine-learning-databases/iris/ (link to iris data set )

https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html ( link to train test split)

https://scikit-learn.org/stable/modules/linear_model.html#ridge-regression (the link to Ridge Classifer)

https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression (the link Logistic Regression)