Matlab Logistic Regression

Programming Assignment: Logistic RegressionMachine Learning
Total points: 100
Note: This assignment is for each individual student to complete on his or her
own.
In this assignment, you will implement logistic regression by using maximum
likelihood estimation and gradient ascent. To get started, you will need to
download the starter code and unzip its contents to the directory where you wish to
complete the assignment.
The problem considered in this assignment is to predict whether a person has
diabetes or not by using the dataset downloaded from
http://networkrepository.com/pima-indians-diabetes.php
You are required to use all eight input features (first eight columns) to build the
logistic regression model.
You are required the complete the following steps:
1. Split the dataset into two, one for training (70%) and another one for testing
(30%).
2. Normalize input features so that the mean value of each feature is 0 and the
standard deviation is 1.
3. Run gradient ascent to learn the logistic regression model using the training
data
4. Evaluate the performance of the model on the testing data
To get started, first open the main script assignmentLogisticR.m. You are
required to modify this script as well as the following seven scripts:
• loadData.m – Function to load and split the dataset into training and
testing sets
• featureNormalize.m – Function to normalize features
• gradientAscent.m – Function to run gradient ascent
• likelihoodFunction.m – Function to compute the log likelihood
and its gradient w.r.t. to the parameters
• evaluateAccuracy.m – Function to evaluate the performance of the
logistic regression model
• predict.m – Function to predict the output
• sigmoid.m – Function to compute sigmoid
You can download Matlab by following the instructions provided in this link:
https://library.sdsu.edu/computers-technology/software/matlab
What to submit?
A zip file that includes the following items:
1) All codes (80 points)
2) A report that includes (20 points):
a. (5 points) All results displayed by the fprintf() or plot function
(e.g., parameter values, accuracy, and convergence graph).
Specify the values of the hyperparameters you used.
b. (10 points) A graph that shows the change of the accuracy as the
learning rate increases (e.g., from 0.001 to 0.1). The maximum
number of iterations for each experimental run can be fixed.
c. (5 points) Describe how your current implementation can be
potentially improved to achieve better performance.