writing Python code to apply various cross-validation strategies to evaluate the performance of a supervised learning model.

This is a group project, this is my part that needs to be worked on
– stratified k-fold cross-validation (k of your choice, you might need to conduct an Internet search to find this method’s syntax)
– Leave-one-out cross-validation
4. Write Python code to apply standard k-fold cross-validation (k of your choice) but make sure the result can be reproduced. Display the cross-validation scores. (10 points)
Assignment description
Create a Jupyter Notebook and save it as P3_G#.ipynb. Create a Word file and save it as ModelEval_G# where G# is your group number.
2. Write Python code to apply the following cross-validation strategies in order to evaluate ONE supervised learning model of your choice on the wine data set. You must include sufficient amount of comments in the Jupyter Notebook to explain (a) what model, (b) which cross-validation strategy, and (c) what parameter values are used. For each cross-validation strategy applied, display the mean of cross-validation scores. (10 points/strategy)
standard k-fold cross-validation with default folds
standard k-fold cross-validation with k of your choice
stratified k-fold cross-validation (k of your choice, you might need to conduct an Internet search to find this method’s syntax)
Leave-one-out cross-validation
Shuffle-split cross-validation (training and test set splits of your choice, 10 iterations)
3. In ModelEval_G# Word file, write a summary about (a) what cross-validation is and when it is usually used; (b) the differences of cross-validation strategies in evaluating your chosen machine learning model’s performance. (20 points)
4. Write Python code to apply standard k-fold cross-validation (k of your choice) but make sure the result can be reproduced. Display the cross-validation scores. (10 points)
5. Download the Jupyter Notebook as HTML.
6. In ModelEval_G# Word file, explain (c) the purpose for splitting the data set into training set, validation set, and test set for certain machine learning models; (d) what is false positive and false negative in binary classification, and why accuracy alone is not a good measure for the machine learning algorithms. (20 points)
Submit: P3_G#.ipynb, P3_G#.html, and ModelEval_G# as a ZIP file. Make sure to include the group report file as well.