Why linear regression is not suitable for classification?

Answer: Linear regression is not suitable for classification because it predicts continuous outcomes rather than discrete classes.

Here’s a detailed explanation of why linear regression is not suitable for classification tasks, presented in bullet points:

• Nature of Output: Linear regression predicts continuous outcomes along a range of values, whereas classification tasks require discrete class assignments for observations.
• Assumption of Continuity: Linear regression assumes that the dependent variable (the variable being predicted) is continuous, which does not align with the discrete nature of class labels in classification problems.
• Inappropriate Predictions: Linear regression can produce predictions outside the valid range of class labels, especially in binary classification tasks where predictions should ideally fall within the [0, 1] range.
• Nonlinear Relationships: Classification problems often involve nonlinear relationships between features and class labels. Linear regression, being a linear model, may not capture these nonlinear relationships effectively.
• Decision Boundary Complexity: Decision boundaries in classification tasks are typically nonlinear and complex. Linear regression models are unable to represent these intricate decision boundaries accurately.
• Alternative Algorithms: Other algorithms such as logistic regression, decision trees, support vector machines, and neural networks are specifically designed for classification tasks. These algorithms can model nonlinear relationships and provide outputs that represent probabilities or discrete class assignments, making them more suitable for classification problems.