Visualizing Machine Learning Outputs

[gavinjalberghini]

In this issue, I would like both of you to create standardized visualizations for the output of your kNN algorithm. In the future, we will be gathering metrics from multiple sources and at multiple points in time. For this sprint, that means your kNN should provide the following:

• A confusion matrix is provided as a graphic or table.
• All metrics that we have reviewed from previous tickets are provided in a table. Be sure to calculate per-class metrics accordingly.
• The execution time of your algorithm.
• The amount of memory expended by your python process.

Resources Python Mem Usage - https://www.geeksforgeeks.org/monitoring-memory-usage-of-a-running-python-program/ ML Metrics - https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234 MC Confusion Matrix - https://www.analyticsvidhya.com/blog/2021/06/confusion-matrix-for-multi-class-classification/ Matplotlib tutorials - https://matplotlib.org/stable/tutorials/introductory/sample_plots.html Matplotlib 3D plots - https://jakevdp.github.io/PythonDataScienceHandbook/04.12-three-dimensional-plotting.html

Acceptance Criteria: The kNN.py algorithm outputs the specified outputs as a file. Run your kNN against small.arff and medium.arff in #files on Discord.

[gavinjalberghini]

V: 2. This has overlap with #1.

[brandonC1234]

Completed - Brandon

[gavinjalberghini]

Finished by all. Excellent work.