Applied Machine Learning Lab 3 Naïve Bayes and Logistic Regression

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Tasks (100 Points)

  1. (45 points) Naïve Bayes1.1 (15 points) Follow the Naïve Bayes code examples to implement all of them. The code & HTML files are required to be AIT736_Lab# _NB.ipynb/HTML.
  2. 1.2 (30 points) Based on 1.1 Naïve Bayes code examples, load the sklearn buit-in datasets load_digits to implement Naïve Bayes classification. Compare the results with the same dataset using logistic regression in 2.1. Briefly explain in the code file. The code & HTML files are required to be AIT736_Lab#_NB_digits.ipynb/HTML.
  1. (45 points) Logistic Regression2.1 (15 points) Follow the Logistic Regression code examples to implement all of them. The code & HTML files are required to be AIT736_Lab#_LogiR.ipynb/HTML.
  2. 2.2 (30 points) Based on 2.1 Logistic Regression code examples, load the sklearn built-in dataset load_wine to implement Logistic Regression classification for this dataset. Compare the results with the same dataset using Naïve Bayes in 1.1. Briefly explain in the code file. The code & HTML files are required to be AIT736_Lab#_LogiR_wine.ipynb/HTML.
  3. •(3 points) Write course information (course number, section #, professor’s name, etc.),lab information, your name, and/or other information as a header in all your code files.
  4. •(3 points) List citations/references to appreciate other’s work in a professional manner.
  5. •(4 points) Name code files as required.

3.(10 points) Format your documents as required below:

Explanation & Answer

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