Artificial Intelligence — CBSE Class 10 board question

The two graphs represent the two sub-types of Supervised Learning models: Classification and Regression.

Figure 1 – Classification Model:
In this model, data is classified into discrete categories/labels. The straight dashed line acts as a decision boundary separating two groups. It works on discrete (non-continuous) data.
Example: Classifying emails as "spam" or "not spam" based on labeled training data.

Figure 2 – Regression Model:
In this model, a continuous curved/straight line is fitted through the data points to predict continuous output values. It works on continuous data.
Example: Predicting the price of a house based on features like number of bedrooms, carpet size, and garage area.

Both models learn from labeled data, which is the defining feature of Supervised Learning.

Source: Chapter 2, Section 2.2 – Sub-categories of Supervised Learning Model

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• The question asks you to identify (name) and explain each model with examples — so you must do all three parts for each figure.
• Figure 1 shows a linear decision boundary → Classification (discrete output).
• Figure 2 shows a curve fitted to points → Regression (continuous output).
• Key distinguisher: discrete output = Classification; continuous output = Regression.
• Always mention that both fall under Supervised Learning (labeled data) — examiners often award a mark for this link.