What is confusion matrix?

Question

Accepted Answer

Confusion matrix is a table used to evaluate the performance of a classification model.

It shows the number of true positives, true negatives, false positives, and false negatives.
It helps in calculating various evaluation metrics like accuracy, precision, recall, and F1 score.
It is useful in identifying which classes are being misclassified and how often.
Example: A confusion matrix for a binary classification model can be represented as follows: | | Predicted Positive | Predicted Negative | |--------------|--------------------|--------------------| | Actual Positive | True Positive (TP) | False Negative (FN) | | Actual Negative | False Positive (FP) | True Negative (TN) |

Popular interview questions of Machine Learning Engineer