In this assignment, we will compare Naive Bayesian and Decision Tree Classification for identifying normal vs

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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/ Page 1 BU MET CS-677: Data Science With Python, v.2.0 CS-677 Assignment: NB, Trees & RF 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. Page 2 BU MET CS-677: Data Science With Python, v.2.0 CS-677 Assignment: NB, Trees & RF • 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 Page 3 BU MET CS-677: Data Science With Python, v.2.0 CS-677 Assignment: NB, Trees & RF 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 Page 4 BU MET CS-677: Data Science With Python, v.2.0 CS-677 Assignment: NB, Trees & RF 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