Consider a class with 5 students. Student academic performance can be classified

Question

Consider a class with 5 students. Student academic performance can be classified to 3 classes: Class A, B and C. For student academic performance, the following data sets are obtained: a classification training dataset (Td), and predicted classified dataset (Cd).

Td = {'a','b','c','a','c'}

Cd = {'a','b','a','a','a'}

Note that each number denotes a class. For example, 'a' is in the class A. Assume that the dataset Cd is obtained by applying some classification algorithm.

Choose one of the classification algorithms and explain it.

Which class has 100% precision?

Which class has 0% recall?

What is support for each class

Explanation / Answer

According to the Given Data, a class is having 5 students. The performance of each students can be categorsied into 3 classes -> A, B C

So the table with classsification training dataset and predicted classified dataset looks like

Termas used: True Positive -> TP, False Positive -> FP, False Negative -> FN, False Positive -> FP.

Tree-based ID3 classification algorithm is used to analyze the data. The precision is calculated as (TP / (TP + FP)) and the Recall is calculated as (TP / TP + FN)).

Let us design the Matrix using ID3 algorithm

Conside for Student 1: Td says it is Class A & Cd says it is Class A. So by considering all 5 students, for Class A total predicted number of classes are Two, for Class B total predicated number of class is One, for Class C total predicted number of class is Zero.

For Class A, Precision is (2/ (2+0))*100 = 100%

For Class B, Precision is (1/ (1+0))*100 = 100%

For Class C, Precision is (0 / (0+2))*100 = 0%

Therefore, Classes A & have 100% Precision and Class C has 0% Recall.

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