Given is the result of the model performance. Help me with this MCQ
$begingroup$
You also evaluate your model on the test set, and find the following:
Human-level performance 0.1%
Training set error 2.0%
Dev set error 2.1%
Test set error 7.0%
What does this mean? (Check the two best options.)
You have underfit to the dev set.
You should get a bigger test set.
You should try to get a bigger dev set.
You have overfit to the dev set.
machine-learning training
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
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bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
You also evaluate your model on the test set, and find the following:
Human-level performance 0.1%
Training set error 2.0%
Dev set error 2.1%
Test set error 7.0%
What does this mean? (Check the two best options.)
You have underfit to the dev set.
You should get a bigger test set.
You should try to get a bigger dev set.
You have overfit to the dev set.
machine-learning training
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
$endgroup$
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
You also evaluate your model on the test set, and find the following:
Human-level performance 0.1%
Training set error 2.0%
Dev set error 2.1%
Test set error 7.0%
What does this mean? (Check the two best options.)
You have underfit to the dev set.
You should get a bigger test set.
You should try to get a bigger dev set.
You have overfit to the dev set.
machine-learning training
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
$endgroup$
You also evaluate your model on the test set, and find the following:
Human-level performance 0.1%
Training set error 2.0%
Dev set error 2.1%
Test set error 7.0%
What does this mean? (Check the two best options.)
You have underfit to the dev set.
You should get a bigger test set.
You should try to get a bigger dev set.
You have overfit to the dev set.
machine-learning training
machine-learning training
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
asked Oct 20 '18 at 14:33
Mihir ThakkarMihir Thakkar
11
11
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
I think answer are options 3) and 4)
Clearly there seems to be overfiting towards dev set as the dev set error is low and almost equal to training set error.
And increasing the number of samples helps minimizing overfiting problem..
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
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add a comment |
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1 Answer
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$begingroup$
I think answer are options 3) and 4)
Clearly there seems to be overfiting towards dev set as the dev set error is low and almost equal to training set error.
And increasing the number of samples helps minimizing overfiting problem..
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
$endgroup$
add a comment |
$begingroup$
I think answer are options 3) and 4)
Clearly there seems to be overfiting towards dev set as the dev set error is low and almost equal to training set error.
And increasing the number of samples helps minimizing overfiting problem..
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
$endgroup$
add a comment |
$begingroup$
I think answer are options 3) and 4)
Clearly there seems to be overfiting towards dev set as the dev set error is low and almost equal to training set error.
And increasing the number of samples helps minimizing overfiting problem..
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
$endgroup$
I think answer are options 3) and 4)
Clearly there seems to be overfiting towards dev set as the dev set error is low and almost equal to training set error.
And increasing the number of samples helps minimizing overfiting problem..
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
answered Oct 20 '18 at 15:18
jimmyjimmy
1012
1012
add a comment |
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