Machine Learning HW2

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CSCE 623: Machine Learning
Your homework will be in jupyter/ipython notebook format – composed of an integrated written portion
(markdown) and python programming. Include any function definitions in this file (one function per cell). Ensure your
cells are well labeled with the steps listed in this instruction set.
You will be using machine learning techniques on several datasets provided. In your answers to written
questions, even if the question asks for a single number or other form of short answer (such as yes/no or which is better: a
or b) you must provide supporting information for your answer to obtain full credit. Use python to perform calculations
or mathematical transformations, or provide python-generated graphs and figures or other evidence that explain how you
determined the answer.
The 3 synthetic datasets (dataset1.csv, dataset2.csv, dataset3.csv) contain observations rows with 2
numerical features (X) and labels (y = 0 or 1). Your task is classification. You will evaluate the efficacy of several
machine learning algorithms (logistic regression, LDA, QDA) using assessments and tools such as accuracy, precision,
recall, F-measure and ROC curves. You will also gain familiarity of working with training and testing sets. You will find
hints in the ISLR book lab for chapter 4.
The Backstory: You are potential vendor trying to convince a customer that your company is capable of
providing machine learning services (including consultation). The customer decides to give you a few datasets and
ask you to develop a report (and associated code) for answering some questions:
For each dataset:
A. Which classification model is the best overall model to use – and why?
B. For that classification model, what is the best threshold parameter setting for c in Pr(Y=1|X=x)>=c … and why?
Comparing 2-feature Logistic Regression, LDA & QDA performance
Each step listed below should correspond to a numerical step identified in your code and a section of text in your report.
One python notebook will be used to handle the entire code and report.
For EACH dataset (dataset1.csv, dataset2.csv, dataset3.csv) follow these steps. Note that you
should interleave the steps (each step contains each dataset) to allow maximum capability to compare
differences among the datasets and the performance of the methods on each dataset:
1. Load the dataset
2. Explore the dataset by plotting the data points from both classes as a function of X1 (x-axis) and X2 (y-axis) scores in
colors according to their labels (for example, one class is red, the other class is blue)
3. Discuss the dataset. What do you notice about the distribution of the data? What can you say about the covariance of
the two classes? Within each class, are the variances for each feature equal? Between classes, are the variances of a
single feature equal? How well are the classes separated? Which predictor do you think will work best under this
condition (Logistic Regression, LDA, or QDA)… and why?
4. Make a function to return a test set and training set from the full dataset. Your split should be parameterized so that
you can declare how many datapoints to use as training. For now, set the number of training points to half and the
number of test points to half. Be careful to ensure that you don’t end up with uneven distributions of classes in each
of the two sets (the training and testing sets should have equivalent proportions from each class).
5. Fit a model for each of the three classifiers (Logistic Regression, LDA, QDA) using only the training set.
6. For each trained classifier, use the test set to determine and store the probabilities for which each classifier believes
the datapoint belongs to class 1: Pr(Y=1|X=x) where x is the datapoint observation. These do not have to be
7. Build a function with the signature: def getROCdata(truthVals,probs,thresholds)
where truthVals is a column vector that contains the correct classification for all test datapoints; probs is a
column vector that contains the probability that the model believes the datapoint to be of class 1; and thresholds
is a vector of probability thresholds to use when deciding to predict that it is
class=1 if Pr(Y=1|X=x)>threshold[i], and class=0 otherwise.
This function should return a pandas dataframe with rowcount = len(thresholds), and a total of 10 columns
named appropriately as outlined below (a through j). Each row includes a probability threshold in the left column
followed by columns containing the 9 performance measures listed below (computed at that probability threshold).
The function should thus return these 10 columns in the dataframe:
a. Probability threshold (from function input)
b. True Positive count
c. False Positive count
d. True Negative count
e. False Negative count
f. True Positive Rate (aka Recall)
g. False Positive Rate
h. Accuracy
i. Precision
j. F-measure
8. For each model, smartly* generate a vector of 100 probability threshold values to test and call your getROCdata
function to obtain the response. There should be 100 rows in the returned dataframe – which represent the values
computed for each of those possible probability thresholds (*note – make sure you choose your range of probabilities
carefully since choosing a probability threshold below the minimum or above the maximum found in the model will
lead to a degenerate prediction set (all predicted positive or all predicted negative).
9. Write code to implement a function for computing the Area under the Curve (AUC) for ROCs and report AUC for
each classifier. You may use mathematical approximations of the piecewise integral to do so (possibly using math
found on the internet). You will need to deal with partial information since the curves may not extend the full range
from 0 to 1 in both True Positive Rate and False Positive Rate. State your assumptions about how you built the AUC
computation in a jupyter notebook markdown cell.
10. Using the ROCdata from your function, for each model (Logistic Reg, LDA, QDA) determine the probability
threshold(s) for which each of the following performance measures is maximized: Accuracy, Precision, Recall, Fmeasure (there might be as many as 4 probability thresholds per classifier). Then report a confusion matrix table of
predicted class vs. true class (like table 4.5 in the text) at each threshold value. Examining the confusion matrices,
explain what tradeoff is occurring when we set a probability threshold differently to maximize each of those
performance measures.
11. Using the response from the getROCdata function, Plot Receiver Operating Characteristics (ROC) curves for each
of the three classifiers on a single plot. Each ROC curve should use a different color. Make your axes labels and
legend appropriately to clearly identify the mapping between color and classifier. Add text to the ROC graph to
annotate points on the ROC graph which represent the maximum Accuracy, Precision, Recall and F-measure points
on the ROC graph for each model. What do you notice about these points? Where are they along the ROC curve?
12. Now answer the Customer’s Questions:
a. For each dataset, describe which model you recommend the school use for their decision-making (and why).
b. Indicate which probability threshold value (or values) you would recommend they set the classifier to use if
they wanted to balance the risk of false positives and false negatives.
Hints… Suggested Python imports:
A note on code comments
In code, good software engineering principles apply: self-documenting code (meaningful function & variable names), additional
comments and whitespace should standard in all code you turn in. You should explain what you are doing in text in the markdown as
well as in the comments within code chunks. A rule of thumb is to have line-level comments in the code cells and save the larger
high-level comments/discussion for the markdown text outside of the cells.

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