What is the outcome of using a classification technique in a spam detector?

Using a classification technique in a spam detector aims to categorize incoming emails into distinct classes, in this case, either 'spam' or 'not spam.' This approach leverages labeled training data where emails are pre-categorized, allowing the model to learn the characteristics that differentiate spam from legitimate messages.

The classification model analyzes various features of the emails, such as keywords, sender information, and patterns, to make predictions about new, unseen emails. By the end of the classification process, each email is assigned to one of the two categories. This binary classification is a fundamental application of machine learning in natural language processing.

Other options are not relevant to the primary function of a spam detector. Continuous data prediction pertains to regression tasks, where the output is a continuous value rather than discrete categories. Segmenting customers is a task related to clustering rather than classification, as it involves grouping based on similarities in purchase behavior without predefined categories. Lastly, identifying the next best action relates more to prediction models in customer relationship management rather than classifying emails.