Difference between parametric and non-parametric models.

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🔹 Parametric Models

• Definition: Models that summarize data using a fixed number of parameters. Once parameters are estimated, the model doesn’t grow in complexity even if you get more data.

• Assumption: They assume a specific functional form or distribution (e.g., linear, Gaussian).

• Examples: Linear Regression, Logistic Regression, Naïve Bayes, Neural Networks (fixed architecture).

• Advantages:

  • Simple, fast to train.

  • Require less data.

  • Easy to interpret (e.g., coefficients in linear regression).

• Disadvantages:

  • Limited flexibility; may underfit if assumptions are wrong.

  • Performance depends heavily on whether the assumed form matches the real data.

🔹 Non-Parametric Models

• Definition: Models that do not assume a fixed form and can grow in complexity with more data. They don’t rely on a fixed number of parameters.

• Assumption: Very few (or none) about the underlying data distribution.

• Examples: k-Nearest Neighbors (kNN), Decision Trees, Random Forests, Support Vector Machines (with kernels).

• Advantages:

  • Very flexible; can capture complex patterns.

  • No strong assumptions about data distribution.

• Disadvantages:

  • Require more data to generalize well.

  • Can be computationally expensive.

  • Risk of overfitting if not controlled (e.g., very deep decision trees).

🔹 Key Differences

Feature	Parametric Models	Non-Parametric Models
Parameters	Fixed, predefined	Flexible, grow with data
Assumptions	Strong (e.g., linearity, normality)	Minimal or none
Flexibility	Low	High
Data requirement	Works well with small datasets	Needs large datasets
Example Algorithms	Linear/Logistic Regression, Naïve Bayes	kNN, Decision Trees, SVM, Random Forest

👉 In short:

• Parametric models = simple, fixed-size, assumption-driven.

• Non-parametric models = flexible, data-driven, complex.

Read More :

What is regularization (L1 vs L2)?

What is gradient descent?

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