For identifying whether an email is spam using ML, which approach should you choose?

When it comes to identifying whether an email is spam, using supervised learning is the most effective approach. This is because spam detection is typically characterized by labeled data; each email in your dataset is already marked as either "spam" or "not spam." Supervised learning algorithms, such as logistic regression, are designed to learn from this labeled data, allowing them to classify new, unseen emails based on patterns recognized during training.

Logistic regression, in particular, is a popular choice for binary classification problems like spam detection. It works by estimating the probability that a given email belongs to a certain category (spam or not spam), based on the features extracted from the email (e.g., the presence of certain keywords, the email's sender, and other attributes). As the model is trained on this historical data, it develops a clear boundary to differentiate between spam and legitimate emails.

Using more complex models, such as neural networks, can also be effective for spam detection due to their ability to capture intricate patterns in the data. However, for a fundamental approach, especially when dealing with interpretability and less computational complexity, logistic regression stands out as a practical choice.

In summary, the correct answer focuses on utilizing supervised learning, specifically through logistic regression, to effectively handle