MCQ IN COMPUTER SCIENCE & ENGINEERING

Detailed explanation-1: -Problem Description. Understanding the problem is a crucial first step in solving any machine learning problem. In this article, we will explore and understand the process of classifying emails as spam or not spam. This is called Spam Detection, and it is a binary classification problem.

Detailed explanation-2: -Logistic regression is one of the most likely and appropriate algorithm used for classification of datasets. In case of classifying a dataset named as spam base the logistic regression is the most versatile decision based approach for detecting spam emails out of a dataset.

Detailed explanation-3: -Case Base Spam Filtering Method: Case base or sample base filtering is one of the popular spam filtering methods. Firstly, all emails both non-spam and spam emails are extracted from each user’s email using collection model.

Detailed explanation-4: -Spam detection is a supervised machine learning problem. This means you must provide your machine learning model with a set of examples of spam and ham messages and let it find the relevant patterns that separate the two different categories. Most email providers have their own vast data sets of labeled emails.

Detailed explanation-5: -Many email services today provide spam filters that are able to classify emails into spam and non-spam email with high accuracy. SVMs will be used to build a spam filter. A SVM classifier will be trained to classify whether a given email, x, is spam (y=1) or non-spam (y=0) .