Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy Precision
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons.
In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the:
- True Positive
- True Negative
- False Positive
- False Negative
- Sensitivity (True positive rate)
- Specificity (True negative rate)
- Accuracy
- Precision
- Draw the confusion matrix
Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons.
In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the:
- True Positive
- True Negative
- False Positive
- False Negative
- Sensitivity (True positive rate)
- Specificity (True negative rate)
- Accuracy
- Precision
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