MCQ IN COMPUTER SCIENCE & ENGINEERING

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COMPUTER SCIENCE AND ENGINEERING

MACHINE LEARNING

Question [CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
The most widely used metrics and tools to assess a classification model are:
A
Confusion matrix
B
Cost-sensitive accuracy
C
Area under the ROC curve
D
All of the above-answer
Explanation: 

Detailed explanation-1: -Accuracy, confusion matrix, log-loss, and AUC-ROC are some of the most popular metrics. Precision-recall is a widely used metrics for classification problems.

Detailed explanation-2: -Area Under Curve(AUC) is one of the most widely used metrics for evaluation. It is used for binary classification problem.

Detailed explanation-3: -Confusion Matrix The matrix’s size is compatible with the amount of classes in the label column. In a binary classification, the matrix will be 2X2. If there are 3 classes, the matrix will be 3X3, and so on. This matrix essentially helps you determine if the classification model is optimized.

Detailed explanation-4: -What exactly are classification metrics? Simply put a classification metric is a number that measures the performance that your machine learning model when it comes to assigning observations to certain classes. Binary classification is a particular situation where you just have to classes: positive and negative.

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