Part I. Given 5 training samples, x1, X2, X3, X4 and x5. Each sample x¡ has two features f1 and f2 and a true label y_true. Assume that a given classifier is applied on the trained samples and predicts the labels y_pred (predicted labels) as indicated in the table below. f1 f2 y_true y_pred x1 5 2 x2 3 2 x3 6 4 1 x4 2 5 1 1 x5 4 6 1 1 Table 1 Note: • The class 0 is the positive class and the class 1 is the negative class. • Use the confusion matrix as studied in the lecture. 1. Compute: • TP = • TN = • FP = • FN = 2. Compute the classification error rate (enter a number with only 1 decimal, e.g. 0.4) 3. Compute the classification accuracy (enter a number with only 1 decimal, e.g. 0.4)
Part I. Given 5 training samples, x1, X2, X3, X4 and x5. Each sample x¡ has two features f1 and f2 and a true label y_true. Assume that a given classifier is applied on the trained samples and predicts the labels y_pred (predicted labels) as indicated in the table below. f1 f2 y_true y_pred x1 5 2 x2 3 2 x3 6 4 1 x4 2 5 1 1 x5 4 6 1 1 Table 1 Note: • The class 0 is the positive class and the class 1 is the negative class. • Use the confusion matrix as studied in the lecture. 1. Compute: • TP = • TN = • FP = • FN = 2. Compute the classification error rate (enter a number with only 1 decimal, e.g. 0.4) 3. Compute the classification accuracy (enter a number with only 1 decimal, e.g. 0.4)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Question
![Part I.
Given 5 training samples, x1, x2, X3, X4 and x5. Each sample x¡ has two features f1 and f2 and a true label y_true. Assume that a given classifier is applied on the
trained samples and predicts the labels y_pred (predicted labels) as indicated in the table below.
f1 f2
y_true
y_pred
x1 5 2
x2 3 2
1
x3 6
4
x4 2
1
1
x5 4
6
1
1
Table 1
Note:
• The class 0 is the positive class and the class 1 is the negative class.
• Use the confusion matrix as studied in the lecture.
1. Compute:
• TP =
• TN =
• FP =
• FN =
2. Compute the classification error rate (enter a number with only 1 decimal, e.g. 0.4)
3. Compute the classification accuracy (enter a number with only 1 decimal, e.g. 0.4)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F2a325d25-2776-468d-b531-3f6adce0ef5a%2F4bbc1a1e-fa61-4a71-985c-ec0e2fb9f962%2F74y65g_processed.png&w=3840&q=75)
Transcribed Image Text:Part I.
Given 5 training samples, x1, x2, X3, X4 and x5. Each sample x¡ has two features f1 and f2 and a true label y_true. Assume that a given classifier is applied on the
trained samples and predicts the labels y_pred (predicted labels) as indicated in the table below.
f1 f2
y_true
y_pred
x1 5 2
x2 3 2
1
x3 6
4
x4 2
1
1
x5 4
6
1
1
Table 1
Note:
• The class 0 is the positive class and the class 1 is the negative class.
• Use the confusion matrix as studied in the lecture.
1. Compute:
• TP =
• TN =
• FP =
• FN =
2. Compute the classification error rate (enter a number with only 1 decimal, e.g. 0.4)
3. Compute the classification accuracy (enter a number with only 1 decimal, e.g. 0.4)
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