MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Evaluation of the performance of a classification model is based on the counts of test records correctly and incorrectly predicted by the model. These counts are tabulated in a table known as a confusion matrix.

Detailed explanation-2: -A Classification Table (aka a Confusion Matrix) describes the predicted number of successes compared with the number of successes actually observed. Similarly, it compares the predicted number of failures with the number actually observed.

Detailed explanation-3: -A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes. It plots a table of all the predicted and actual values of a classifier.

Detailed explanation-4: -A confusion matrix is a table that allows you to visualize the performance of a classification model. You can also use the information in it to calculate measures that can help you determine the usefulness of the model. Rows represent predicted classifications, while columns represent the true classes from the data.

Detailed explanation-5: -A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.