Programming 6 - Logistic Regression

Goal

• Learn to implement logistic regression as multiclass classifiers.

• Improve performance by handling imbalanced classes, reducing variance with regularization, and training with very large data.

Readings

• R1 - Chapter 16 Logistic Regression,  Machine Learning with Python Cookbook 2nd,  Free O'Reilly Learning by FJU ID

Instructions

• Read R1 and practice all the Python codes with the IRIS data.

• Exercise: Change the IRIS with the datasets in Scikit-Learn real-world dataset. Use at least two datasets in your exercise. Modify the Python codes to perform logistic regression on new data. Analyze the results of output, explain the results, and improve the results.

Report: a separate and single web page that you

• • Write the goal of this assignment

  • Explain the logistic regression algorithm. Explain the Scikit-Learn functions of logistic regression.

  • Explain the Python codes. Upload code output results into your report web page. Explain the results of the output results

  • Clearly explain modified code segments written by you. Give some analysis in your report.

  • Give references to this assignment (teacher's readings, your readings)

Submit the web address of your web page to Microsoft Teams.