What is Nonlinear regression? And when to use it?

What is Nonlinear regression

Nonlinear regression is a statistical technique used to model relationships between data points that aren’t straight lines. Unlike linear regression, which assumes a constant slope between variables, nonlinear regression allows for curves, S-shapes, and more complex relationships.

What it does:

  • Fits a nonlinear function to your data points. This function can involve exponents, logarithms, trigonometric functions, or any combination that captures the true relationship.
  • Helps you understand curved or complex patterns in your data that linear regression might miss.
  • Enables you to make more accurate predictions when the relationship between variables isn’t linear.

When to use it:

  • When you observe visible curves or non-linear trends in your data visualizations.
  • When your predictions from linear regression are consistently inaccurate.
  • When you’re studying phenomena known to have non-linear behavior, like population growth or chemical reactions.


  • More complex than linear regression. Finding the right nonlinear function and optimizing its parameters can be computationally expensive and require specialized algorithms.
  • Potential for overfitting. Choosing a too-complex function can lead to a model that memorizes the data without generalizing well to new examples.

How to choose a nonlinear model?

Visualize and Understand Your Data:

  • Plot your data to visualize the relationship between variables. Look for curves, plateaus, or other non-linear patterns.
  • Consider the nature of the variables and the domain knowledge you have. This can suggest potential model shapes.

Explore Common Nonlinear Models:

  • Polynomial Regression: Adds higher-order terms (e.g., x^2, x^3) to capture curves.
  • Exponential Models: Often used for growth or decay processes (e.g., population, radioactive decay).
  • Logistic Models: Model S-shaped curves, common in probabilities and bounded growth.
  • Sigmoid Models: Similar to logistic models, used in neural networks and classification.
  • Power Models: Model relationships between variables with power functions (e.g., y = ax^b).
  • Gompertz Models: Used for modeling sigmoidal growth with an initial exponential phase.
  • Fourier Series: Represent periodic functions using sine and cosine waves.

Consider Theoretical Considerations:

  • If you have theoretical knowledge about the underlying process, it can suggest specific model forms.
  • For example, chemical reactions often follow exponential or power laws.
  • Biological processes might exhibit logistic growth.

Experiment and Evaluate:

  • Try different models and compare their performance using metrics like:
    • Mean squared error (MSE)
    • R-squared
    • Akaike Information Criterion (AIC)
    • Bayesian Information Criterion (BIC)
  • Visually inspect model fits: Plot the fitted model against data points to assess how well it captures the relationship.
  • Consider model complexity: Simpler models are often preferred if they provide similar performance to more complex ones.

Refine and Validate:

  • Adjust model parameters to improve fit.
  • Validate models on independent datasets to assess generalizability.
  • Consider model interpretability: If understanding the underlying relationship is important, choose a model with more intuitive parameters.

How to evaluate a nonlinear model?

Visual Inspection:

  • Plot the fitted model curve against the actual data points.
  • Look for:
    • Close alignment between the curve and data points.
    • Random scatter of residuals (differences between predicted and actual values) without clear patterns.
  • S-shaped curves might require transformation (e.g., log transformation) to assess linearity of residuals.

Goodness-of-Fit Statistics:

  • R-squared: Measures the proportion of variance in the dependent variable explained by the model. Higher values (closer to 1) indicate better fit.
  • Adjusted R-squared: Penalizes for model complexity, useful for comparing models with different numbers of parameters.
  • Mean Squared Error (MSE): Average squared difference between predicted and actual values. Lower values indicate better fit.
  • Root Mean Squared Error (RMSE): Square root of MSE, in the same units as the dependent variable, for easier interpretation.

Residual Analysis:

  • Plot residuals against predicted values and independent variables.
  • Look for:
    • Random scatter around zero, indicating no systematic patterns.
    • Constant variance of residuals across the range of predicted values (homoscedasticity).
    • If patterns or non-constant variance are present, consider model adjustments or transformations.

Information Criteria:

  • Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC): Balance model fit and complexity. Lower values indicate better fit while penalizing for more parameters. Useful for comparing non-nested models.

Validation of Model:

  • Assess model performance on independent datasets (not used for fitting) to evaluate generalizability and avoid overfitting.
  • If performance drops significantly on new data, the model might be overfitting the training data.

Domain Knowledge:

  • Consider whether model predictions align with theoretical understanding and expectations for the specific domain.
  • Incorporate expert knowledge to assess the plausibility of parameter estimates and model behavior.

Important point to Remember:

  • Evaluation is an iterative process.
  • Refine the model, try different estimation methods, or consider alternative model forms if initial evaluation suggests issues.
  • Balance statistical measures with visual inspection and domain knowledge for comprehensive assessment.

Advantages of nonlinear models:

  1. Capture complex relationships: Unlike linear models which assume straight line relationships, nonlinear models can capture more intricate patterns in data, like curves, S-shapes, or exponential trends. This makes them suitable for modelling a wider range of real-world phenomena, from population growth to chemical reactions.
  2. Improved accuracy: When the true relationship between variables is nonlinear, using a nonlinear model can lead to significantly more accurate predictions compared to a linear model. This can be crucial for tasks like forecasting, anomaly detection, or optimizing complex systems.

Disadvantages of nonlinear models:

  1. Increased complexity: Nonlinear models are often more complex than linear models, requiring more sophisticated algorithms for fitting and potentially leading to computational challenges. This can also make them less interpretable, meaning it’s harder to understand the relationships they capture.
  2. Overfitting risk: Nonlinear models are more prone to overfitting, meaning they can memorize the training data without generalizing well to new examples. Careful model selection, regularization techniques, and validation on unseen data are crucial to avoid this.

Overall, nonlinear models offer greater flexibility and potentially more accurate predictions for complex relationships, but come with the trade-offs of increased complexity and overfitting risk. Choosing the right model type depends on the specific problem, data characteristics, and desired balance between accuracy and interpretability.tunesharemore_vertadd_photo_alternate

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