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# Non-Linear Regression in R – multivariate adaptive regression in R

Non-linear regression is a type of statistical analysis that is used to model relationships between variables that are not linear. In other words, it is used to model relationships where the change in one variable is not directly proportional to the change in another variable. One popular method of non-linear regression is using multivariate adaptive regression splines (MARS).

MARS is a type of regression algorithm that can be used to model complex relationships between multiple variables. It works by fitting a series of piecewise linear functions to the data, and then combining them to create a final model. This allows MARS to capture non-linear relationships in the data that may not be captured by traditional linear regression methods.

In R, there are several packages available for building MARS models, such as the ‘earth’ package. This package provides functions for creating and training MARS models, as well as functions for evaluating the performance of the model.

The process of building a MARS model in R typically involves the following steps:

1. Prepare the data: The first step is to prepare the data for the model. This may involve cleaning the data, splitting it into training and testing sets, and scaling the variables.
2. Define the model: The next step is to define the structure of the model, including the number of knots to be used in the piecewise linear functions.
3. Train the model: The model is trained using the prepared data. The model will fit a series of piecewise linear functions to the data, and then combine them to create a final model.
4. Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.
5. Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data.

By using Multivariate Adaptive Regression in R, you can model non-linear relationship and get accurate predictions. It’s particularly useful when you have multiple variables and complex relationships between them. It’s also a good choice when you don’t know the functional form of the relationship between the variables and you want to find it out from the data.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: Non-Linear Regression in R – multivariate adaptive regression in R.

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