Parametric vs Nonparametric Machine Learning Models #ai #machinelearning #computerscience

When designing models, your choice affects how the data is understood:

Parametric Models
• Assume a fixed form (e.g., linear regression)
• Few parameters, fast & efficient
• Risk: miss real patterns if assumptions don’t fit

Non-Parametric Models
• No strict assumptions about distribution
• Flexible (e.g., k-NN, decision trees)
• Need more data & computation to generalize

Parametric = efficiency with assumptions
Non-parametric = flexibility with higher cost

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