What is worse, type 1 error or type 2 error?
Question
TheDataMonk
Follow Me
About TheDataMonkGrand Master
I am the Co-Founder of The Data Monk. I have a total of 6+ years of analytics experience 3+ years at Mu Sigma 2 years at OYO 1 year and counting at The Data Monk I am an active trader and a logically sarcastic idiot :)Follow Me
Answers ( 11 )
You will encounter both the errors while solving a Classification problem.
You will always produce a confusion matrix while solving a classification problem,
irrespective of the algorithm you use.
A confusion matrix is a 2*2 matrix consisting of True Positives, True Negatives, False Positives, False Negatives.
Type 1 Error corresponds to False Positives.
Type 2 Error corresponds to False Negatives.
To say in layman terms, Type 1 Error is predicting something True when it is actually False.
For Example – Predicting a person will default on the Loan when in reality he hasn’t defaulted on the Loan.
Type 2 Error is predicting something False when it is actually True.
For Example – Predicting a person will not default on the Loan when in reality he has defaulted on the Loan.
Now, we can see that Type 2 error is worse than Type 1 error because if we predict that someone will default on a loan
and he does not default, we are not at much of a loss.
But if we predict that someone will not default and in reality the person defaults, than we as a organisation
will certainly be at loss.
Sorry it a private answer.
Ananya, Don’t you think it depends on the use case as well?
Type I error aka False Positive Rate is nothing but, Out of all the actual negatives how many the model wrongly predicted them as positive.
In a more simple terms referring to the above example if the model wrongly predicts the unmarried people as married then it will be a Type I Error.
Type II Error aka False Negative Rate is nothing but Out of all the actual positives how many the model wrongly predicted them as negative.
In more simple terms referring to the above example if the model wrongly predicts married people as unmarried then it will be a Type II Error.
The worst among the two cannot be decided. It depends on the domain you are working on.
For example if we take the above confusion matrix then predicting married people as unmarried is completely wrong. In this case we can say that Type II Error is worse than Type I Error.
it depends on the use case. for example if we want to test for cancer , its comparatively ok if someone doesn’t have cancer but still goes for therapy which can be revealed later that the person is fit but it would be absolutely terrible if someone has cancer and we are not able to detect it.so we should minimize false negative in this case.
when it comes to spam classification we definitely want to reduce the false positive since we don’t want an important mail to go into spam but its ok if occasionally some spam comes into regular mail.
Very much use-case sensitive. In general type-2 error is worse than type-1 error
Type 2 error is much worse than type 1 error.
for example if a person have cancer and model predicting he does not have is the worst case than predicting he has but actually he doesn’t.
Type 2 error is worse compared to Type 1 error.
Example: Whether or not the building got fire.
Null hypothesis(H0): Not serious, everything is normal
Alternate Hypothesis(Ha): Serious fire.
Type 2 is “not rejecting the null hypothesis when it is actually false”
What does it mean the null hypothesis is false?
— It is a serious fire. The building got fire.
Not rejecting the null hypothesis: Still concluding there is no serious fire even though actually there is fire.
So, the people in the building get injured and it is major damage.
The short answer to this question is that it really depends on the situation. Type I error corresponds to a “false positive” test result and a Type II error means a false negative test result.
Suppose you are designing a medical screening for a disease. A false positive of a Type I error may give a patient some anxiety, but this will lead to other testing procedures which will ultimately reveal the initial test was incorrect. In contrast, a false negative from a Type II error would give a patient the incorrect assurance that he or she does not have a disease when he or she in fact does. Here, Type I error is good.
Now suppose that someone had been put on trial for murder. The null hypothesis here is that the person is not guilty. A Type I error would occur if the person were found guilty of a murder that he or she did not commit, which would be a very serious outcome for the defendant. On the other hand, a Type II error would occur if the jury finds the person not guilty even though he or she committed the murder, which is a great outcome for the defendant but not for society as a whole. Here we see the value in a judicial system that seeks to minimize Type I errors.
Type 2 error is worse as if we predict aa healthy person as cancer patient then the person got tested as get negative results after tests but if we predict cancer patient as healthy then it might cost his/her life
By thinking in terms of false positive and false negative results, we are better equipped to consider which of these errors are better—Type II seems to have a negative connotation, for good reason.
Suppose you are designing a medical screening for a disease. A false positive of a Type I error may give a patient some anxiety, but this will lead to other testing procedures which will ultimately reveal the initial test was incorrect. In contrast, a false negative from a Type II error would give a patient the incorrect assurance that he or she does not have a disease when he or she in fact does. As a result of this incorrect information, the disease would not be treated. If doctors could choose between these two options, a false positive is more desirable than a false negative.
Now suppose that someone had been put on trial for murder. The null hypothesis here is that the person is not guilty. A Type I error would occur if the person were found guilty of a murder that he or she did not commit, which would be a very serious outcome for the defendant. On the other hand, a Type II error would occur if the jury finds the person not guilty even though he or she committed the murder, which is a great outcome for the defendant but not for society as a whole. Here we see the value in a judicial system that seeks to minimize Type I errors.