Regression vs. Classification Use Cases

Regression vs. Classification in model labeling and training

In labeling, regression involves assigning continuous numerical values, while classification involves categorizing data into discrete labels. During training, regression models learn to predict numerical values, whereas classification models learn to categorize data points into predefined classes.

Classification use cases

  • Emotion Categorization: Classification excels in distinguishing distinct emotional states, like identifying happiness, sadness, or surprise based on linguistic or physical expression cues.
  • Binary Emotional Analysis: Useful in binary scenarios such as detecting presence or absence of specific emotional reactions, like engagement or disengagement in a learning environment.
  • Multi-Emotional Identification: Perfect for classifying a range of emotions in complex scenarios, like understanding varied customer reactions from satisfied to dissatisfied based on their verbal and non-verbal feedback.

Regression use cases

  • Intensity Measurement: Regression is apt for quantifying the intensity or degree of emotional responses, such as assessing the level of stress or joy from vocal or facial cues.
  • Emotional Progression Tracking: Ideal for monitoring the fluctuation of emotional states over time, like tracking the development of engagement or anxiety in therapy sessions.

In essence, regression models in emotional expression analysis assign continuous values representing intensities or degrees, while classification models categorize expressions into distinct states or reactions.