Explain type 1 error in simplest terms
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Answers ( 14 )
You will encounter this error 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.
To say in layman terms, 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 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.
Note: Please correct me if I am wrong.
Type 1 errors – often assimilated with false positives – happen in hypothesis testing when the null hypothesis is true but rejected. The null hypothesis is a general statement or default position that there is no relationship between two measured phenomena.
Type I error is generally rejecting the null hypothesis when it is true.
It is calculated by alpha
During a hypothesis test, if alpha is set to 0.05, then it means there is 5% chance of making a Type I error for the test.
For eg, a judge convicting an innocent in a court trail can be termed as a Type I error made.
A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis.
This means that your report that your findings are significant when in fact they have occurred by chance.
For example, let’s look at the trail of an accused criminal. The null hypothesis is that the person is innocent, while the alternative is guilty. A Type I error in this case would mean that the person is not found innocent and is sent to jail, despite actually being innocent.
In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis.
A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. This means that your report that your findings are significant when in fact they have occurred by chance.
Confusion Matrix : consists of True Positive, True Negative, False Positive and False Negative.
Type 1 error: when Predicting something TRUE when actually it is False. (False Positive)
Type 1 error: when Predicting something TRUE when Actually it is False. (False Positive)
In other words, rejecting null hypothesis when it is actually true.
Concluding a man is pregnant when they are actually not – Type 1 Error
Here the null hypothesis is:
H0 – Not pregnant
And the alternative hypothesis:
Ha – Pregnant
Type 1 error occurs when the null hypothesis is rejected when it is actually true.
Here, the null hypothesis (a man is not pregnant) is true.
But it concluded that he is pregnant. So null hypothesis is rejected.
In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis.
Type 1 error is predicting false positive means for eg in cancer prediction test predicting a healthy person as cancer patient
A type I error occurs during hypothesis testing when a null hypothesis is rejected, even though it is accurate and should not be rejected.
The null hypothesis assumes no cause and effect relationship between the tested item and the stimuli applied during the test.
A type I error is “false positive” leading to an incorrect rejection of the null hypothesis.
For example, let’s look at the trail of an accused criminal. The null hypothesis is that the person is innocent, while the alternative is guilty. A Type I error in this case would mean that the person is not found innocent and is sent to jail, despite actually being innocent.
Type 1 error is also called false positive. That is when something is false but somehow you have proven that true. In other words, When you reject the true null hypothesis.
Type 1 error is the one when you reject the true null hypothesis. It is also called the false positive. That is when something is wrong but you proved it true.