Description
1.1 Chapter 3
Exercises: 2,3,5,6,7,8,12
2 Practicum Problems
These problems will primarily reference the lecture materials and the examples
given in class using Python. It is suggested that a Jupyter/IPython notebook
be used for the programmatic components.
2.1 Problem 1
Load the iris sample dataset from sklearn (load iris()) into Python using a
Pandas dataframe. Induce a set of binary Decision Trees with a minimum of
2 instances in the leaves, no splits of subsets below 5, and an maximal tree
depth from 1 to 5 (you can leave other parameters at their defaults). Which
depth values result in the highest Recall? Why? Which value resulted in the
lowest Precision? Why? Which value results in the best F1 score? Explain the
difference between the micro/macro/weighted methods of score calculation.
2.2 Problem 2
Load the Breast Cancer Wisconsin (Diagnostic) sample dataset from the UCI
Machine Learning Repository (The discrete version at: breast-cancerwisconsin.data) into Python using a Pandas dataframe. Induce a binary
Decision Tree with a minimum of 2 instances in the leaves, no splits of subsets
below 5, and a maximal tree depth of 2 (use the default Gini criterion). Calculate
the Entropy, Gini, and Misclassification Error of the first split – what is the
Information Gain? What is the feature selected for the first split, and what
value determines the decision boundary?
2.3 Problem 3
Load the Breast Cancer Wisconsin (Diagnostic) sample dataset from the UCI
Machine Learning Repository (The continuous version at: wdbc.data) into
Python using a Pandas dataframe. Induce the same binary Decision Tree
as above (now using the continuous data) but perform a PCA dimensionality
reduction beforehand. Using only the first principal component of the data for
a model fit, what is the F1, Precision, and Recall of the PCA-based single factor
model compared to the original (continuous) data? Repeat using the first and
second principal components. Using the Confusion Matrix, what are the values
for FP and TP as well as FPR/TPR? Is using continuous data in this case
beneficial within the model? How?
2.4 Problem 4
Simulate a binary classification dataset with a single feature using a mixture of
normal distributions with NumPy (Hint: Generate two data frames with the
random number and a class label, and combine them together). The normal
distribution parameters (np.random.normal) should be (5,2) and (-5,2) for
the pair of samples. Induce a binary Decision Tree of maximum depth 2, and
obtain the threshold value for the feature in the first split. How does this value
compare to the empirical distribution of the feature?
Prof. Panchal:
Wed. 6:45PM-9:35PM
CS 422 – Data Mining
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