Accuracy metrics in Model Validation: Precision, Recall, F1 Score, True Positive, False Positive, False Negative, Interview questions
Most frequently asked data science interview questions for Accuracy metrics: Precision, Recall, TP, FP, FN
What are true positives?
The true positive rate is the number of correctly classified positive examples divided by the total number of positive examples.
What are false positives?
The false positive rate is the number of incorrectly classified positive examples divided by the total number of negative examples.
What are false negatives?
A false negative is when a test result incorrectly indicates that a particular condition or attribute is not present.
What is Precision? And How to calculate it?
Precision is then the true positive rate divided by the sum of the true positive rate and the false positive rate.
Precision = TP / (TP + FP)
Where,
- TP is the number of true positives,
- FP is the number of false positives
What is Recall? And How to calculate Recall?
Recall is the true positive rate divided by the sum of the true positive rate and the false negative rate.
Recall = TP / (TP + FN)
Where,
- TP is the number of true positives,
- FN is the number of false negatives.
What is F1 Score? And How to calculate F1 Score?
The F1 score is a measure of a classifier's accuracy.
F1 score is calculated by taking the harmonic mean of precision and recall.
F1 = 2 * (precision * recall) / (precision + recall)