Type 2 error in simple terms
Confusion Matrix is one of those concepts which is a bit confusing but is also asked to candidates in order to check their clarity of concepts.
There could be questions like,”Explain a model which is 99% accurate but still of no use to the company”
The answer lies to the understanding of the concepts of confusion matrix
Type 2 error in simple terms
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 2 Error corresponds to False Negatives.
To say in layman terms, predicting something False when it is actually True.
For Example – Predicting a person will not default on the Loan when in reality has defaulted on the Loan.
The above has been contributed by the user spawlaw007
Username – smk has defined the above question in the following way
1) A statistically significant result cannot prove that a research hypothesis is correct (as this implies 100% certainty). Because a p-value is based on probabilities, there is always a chance of making an incorrect conclusion regarding accepting or rejecting the null hypothesis (H0)
2) Anytime we make a decision using statistics there are four possible outcomes, with two representing correct decisions and two representing errors
TYPE II ERROR:
1) A type II error is also known as a false negative and occurs when a researcher fails to reject a null hypothesis which is really false. Here a researcher concludes there is not a significant effect when actually there really is
2) You can decrease the risk of type II error by having a large sample size
TYPE I ERROR:
1)A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis
2) This means that your report that your findings are significant when in fact they have occurred by chance
3) The probability of making a type I error is represented by your alpha level (α), which is the p-value below which you reject the null hypothesis EXAMPLE: A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.
You can reduce your risk of committing a type I error by using a lower value for p. For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error)
There are 15+ users who have answered this questions, you can explore all the answers to have a clear understanding of Type-2 error
Link to question – https://thedatamonk.com/question/explain-type-2-error-in-simple-terms/
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