Completed

Machine Learning -- 2

Implement Logistic Regression from scratch

using a programming language of your choice. As part of your

implementation of logistic regression (LR), you will code the Gradient

Descent Algorithm that we discussed in class to find out the parameters

for Θ. One way to verify gradient descent is working as expected is to

monitor the value of J(Θ) whether it is decreasing with each training

iteration.

Please address the subparts in each section to receive full credit and

justify what you did in your implementation as well as the results you

obtained.

You will use observational data collected from caregivers of people with

dementia on their sleep quality. There are 20 independent variables and 1

dependent variable in the dataset and you will be specified which

variables to use (as source and target) to train your model. The dataset is

included in the Assignment with filename sleepQualityFinal.csv. You may

use any train/val/test split ratio and/or K-Fold cross-validation as you

see fit. Note that your model should never be exposed to your testing

(hold-out) data at train time.

1. Logistic Regression using One Feature

a. Train your Logistic Regression (LR) model to predict sleep_quality

using the amount of sleep (amountAsleep) as input feature.

b. Evaluate the performance of your model on the training set and

hold-out (testing) set using a metric discussed in class (e.g.,

precision, recall, F1 score, and confusion matrix). Discuss your

results.

2. Logistic Regression with multiple variables and training

samples

a. Train your LR model to predict the sleep_quality using the 12

features below:

swsLengthHR, swsTimeHR, swsLengthT, swsTimeT, decreasePercentageT,

swsTimeM, swsLengthM, decreasePercentageM, amountAsleep,

amountAwake, sleepEfficiency, epochPeakCounter.

b. Train your LR model to predict sleep_quality using forward selection

to select the most significant features in the dataset as input

variables. Which subset of features gave you the best performance?

What are your thoughts on these features getting selected?

c. Compare the performance of the model built using all of the

features in (2a) with the model trained using the selected features

(2b) . Which set of features performed better?

d. Train your LR model using training sample size within the range

{50, 100, 150} and the features selected in (2b) and compute

cross-entropy loss (log loss) on the same hold-out set and plot your

results (log-loss on y-axis and train sample size on x-axis). What is

the impact of increasing training sample size? Justify why the

log-loss decreases, increases or doesn’t change at all.

Regularization and Feature Scaling:

i. For the best performing model in Q 2 (Model from 2c), does

regularization improve the performance?

ii. Does Feature Scaling improve the performance for the model

in Q 3a?

Skills: Python, C++ Programming

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