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Programming Exercise 2: Logistic Regression
Machine Learning October 30, 2011
Introduction
In this exercise, you will implement logistic regression and apply it to two different datasets. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave to change to this directory before starting this exercise. You can also find instructions for installing Octave on the “Octave Installation” page on the course website.
Files included in this exercise ex2.m - Octave script that will help step you through the exercise ex2 reg.m - Octave script for the later parts of the exercise ex2data1.txt - Training set for the first half of the exercise ex2data2.txt - Training set for the second half of the exercise submit.m - Submission script that sends your solutions to our servers mapFeature.m - Function to generate polynomial features plotDecisionBounday.m - Function to plot classifier’s decision boundary [ ] plotData.m - Function to plot 2D classification data [ ] sigmoid.m - Sigmoid Function [ ] costFunction.m - Logistic Regression Cost Function [ ] predict.m - Logistic Regression Prediction Function [ ] costFunctionReg.m - Regularized Logistic Regression Cost indicates files you will need to complete 1
Throughout the exercise, you will be using the scripts ex2.m and ex2 reg.m. These scripts set up the dataset for the problems and make calls to functions that you will write. You do not need to modify either of them. You are only required to modify functions in other files, by following the instructions in this assignment.
Where to get help
The exercises in this course use Octave,1 a high-level programming language well-suited for numerical computations. If you do not have Octave
Machine Learning October 30, 2011
Introduction
In this exercise, you will implement logistic regression and apply it to two different datasets. Before starting on the programming exercise, we strongly recommend watching the video lectures and completing the review questions for the associated topics. To get started with the exercise, you will need to download the starter code and unzip its contents to the directory where you wish to complete the exercise. If needed, use the cd command in Octave to change to this directory before starting this exercise. You can also find instructions for installing Octave on the “Octave Installation” page on the course website.
Files included in this exercise ex2.m - Octave script that will help step you through the exercise ex2 reg.m - Octave script for the later parts of the exercise ex2data1.txt - Training set for the first half of the exercise ex2data2.txt - Training set for the second half of the exercise submit.m - Submission script that sends your solutions to our servers mapFeature.m - Function to generate polynomial features plotDecisionBounday.m - Function to plot classifier’s decision boundary [ ] plotData.m - Function to plot 2D classification data [ ] sigmoid.m - Sigmoid Function [ ] costFunction.m - Logistic Regression Cost Function [ ] predict.m - Logistic Regression Prediction Function [ ] costFunctionReg.m - Regularized Logistic Regression Cost indicates files you will need to complete 1
Throughout the exercise, you will be using the scripts ex2.m and ex2 reg.m. These scripts set up the dataset for the problems and make calls to functions that you will write. You do not need to modify either of them. You are only required to modify functions in other files, by following the instructions in this assignment.
Where to get help
The exercises in this course use Octave,1 a high-level programming language well-suited for numerical computations. If you do not have Octave