Description
In this assignment, we will compare Naive Bayesian and Decision Tree Classification for identifying normal vs. non-normal
fetus status based on fetal cardiograms.
For the dataset, we use ”fetal cardiotocography data set” at
UCI:
https://archive.ics.uci.edu/ml/datasets/Cardiotocography
Dataset Description: From the website: ”2126 fetal cardiotocograms (CTGs) were automatically processed and the
respective diagnostic features measured. The CTGs were also
classified by three expert obstetricians and a consensus classification label assigned to each of them. Classification was both
with respect to a morphologic pattern (A, B, C. …) and to a
fetal state (N, S, P). Therefore the dataset can be used either
for 10-class or 3-class experiments.”
We will focus on the ”fetal state”. We will combine labels
”S” (suspect) and ”P” (pathological) into one class ”A” (abnormal). We will focus on predicting ”N” (normal) vs. ”A”
(”Abnormal”). For a detailed description of features, please
visit the above website.
The data is an Excel (not csv) file. For ways to process excel
files in Python, see https://www.python-excel.org/
You will use the following subset of 12 numeric features:
1. LB – FHR baseline (beats per minute)
2. ASTV – percentage of time with abnormal short term variability
3. MSTV – mean value of short term variability
4. ALTV – percentage of time with abnormal long term variability
5. MLTV – mean value of long term variability
6. Width – width of FHR histogram
7. Min – minimum of FHR histogram
8. Max – Maximum of FHR histogram
9. Mode – histogram mode
10. Mean – histogram mean
11. Median – histogram median
12. Variance – histogram variance
You will consider the following set of 4 features depending on
your facilitator group.
• Group 1: LB, ALTV, Min, Mean
• Group 2: ASTV, MLTV, Max, Median
• Group 3: MSTV, Width, Mode, Variance
• Group 4: LB, MLTV, Width, Variance
For each of the questions below, these would be your features.
Question 1:
1. load the Excel (”raw data” worksheet) data into Pandas
dataframe
2. combine NSP labels into two groups: N (normal – these
labels are assigned) and Abnormal (everything else) We
will use existing class 1 for normal and define class 0 for
abnormal.
Question 2: Use Naive Bayesian NB classifier to answer
these questions:
1. split your set 50/50, train NB on Xtrain and predict class
labels in Xtest
2. what is the accuracy?
3. compute the confusion matrix
Question 3: Use Decision Tree to answer these questions:
1. split your set 50/50, train NB on Xtrain and predict class
labels in Xtest
2. what is the accuracy?
3. compute the confusion matrix
Question 4: Recall that there are two hyper-parameters in
the random forest classifier: N – number of (sub)trees to use
and d – max depth of each subtree
Use Random Forest classifier to answer these questions:
1. take N = 1, . . . , 10 and d = 1, 2, . . . , 5. For each value of
N and d, split your data into Xtrain and Xtest, construct
a random tree classifier (use ”entropy” as splitting criteria
– this is the default) Train you r classifier on Xtrain and
compute the error rate for Xtest
2. plot your error rates and find the best combination of N
and d.
3. what is the accuracy for the best combination of N and k?
4. compute the confusion matrix using the best combination
of N and d
Question 5: Summarize your results for Naive Bayesian,
decision tree and random forest in a table below and discuss
your findings.
Model TP FP TN FN accuracy TPR TNR
naive bayesian
decision tree
random forest