Non-Linear Regression in R – conditional regression trees in R

Non-Linear Regression is a type of regression that can be used to model complex relationships between variables. Unlike linear regression, where the relationship between the predictor and response variables is represented by a straight line, non-linear regression models can represent more complex relationships using different mathematical functions.

One type of non-linear regression is called Conditional Inference Regression Trees or CART for short, which can be used to model non-linear relationships in a hierarchical way. It is a tree-based algorithm that works by recursively dividing the data into smaller subsets, and fitting a simple linear regression model to each subset.

In R, you can use the “party” package to perform conditional regression trees. The first step is to install and load the package in R. Then, you will need to prepare your data by splitting it into training and test sets.

Next, you will use the “ctree” function to fit a conditional regression tree model to your data. This function takes several inputs, such as the predictor variables and the response variable. It also allows you to specify the complexity of the model by setting the value of the “control” parameter.

Once the model is fit, you can use the “predict” function to make predictions on new data. You can also use the “plot” function to visualize the tree structure of the model.

It is important to keep in mind that the tree-based models are not always suitable for all types of data, the performance depends on the nature of the data. Also, it’s important to consider that the tree-based models are prone to overfitting and it’s important to use techniques like pruning or cross-validation to avoid that.

In summary, Conditional Inference Regression Trees or CART is a type of non-linear regression that can be used to model complex relationships between variables. The “party” package in R provides an easy and efficient way to fit CART models and make predictions. The tree-based model works by recursively dividing the data into smaller subsets, and fitting a simple linear regression model to each subset. However, it’s important to keep in mind that the tree-based models are not always suitable for all types of data and it’s important to use techniques like pruning or cross-validation to avoid overfitting.

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 – conditional regression trees in R.