Answer:
Given,
P = (22, 1, 42, 10)
Q = (20, 0, 36, 8)
a. Formula for Euclidean Distance :
distance = ((p1-q1)^2 + (p2-q2)^2 + ... + (pn-qn)^2)^(1/2)
Now,
distance = ( (22-20)^2 + (1-0)^2 + (42 - 36)^2 + (10-8)^2) ) ^(1/2)
=( (2)^2 + (1)^2 + (6)^2 + (2)^2 ) ) ^(1/2)
=(4+1+36+4)^(1/2)
=45^(1/2)
Distance = 6.7082
b.Manhattan distance :
d = |x1 - x2| + |y1 - y2|
d = |22- 20| + |1 - 0|
d = |2| + |1|
Explanation:
Answer:
Training data is used to fine-tune the algorithm’s parameters and evaluate how good the model is
Explanation:
The statement about datasets used in Machine Learning that is NOT true is "Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is."
This is based on the fact that a Training dataset is a process in which a dataset model is trained for corresponding it essentially to fit the parameters.
Also, Testing the dataset is a process of examining the performance of the dataset. This refers to hidden data for which predictions are determined.
And Validation of dataset is a process in which results are verified to perfect the algorithm's details or parameters
1. true
2. pixel
3. raster images are built with pixels
4. false
5. image size
6. rollover
7. sharp with clear details
8. 78px
9. 24in
10. scalability
Answer:
I believe the answer is B
Explanation:
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