Non-Linear Regression in R – cubist algorithm in R

Non-Linear Regression is a type of regression that can be used to model complex relationships between variables. One type of non-linear regression is called the Cubist algorithm, which is a machine learning algorithm that can be used to make predictions in complex datasets. It is a hybrid algorithm that combines the strengths of both linear regression and decision trees.

The Cubist algorithm works by building a ensemble of models, each one is a simple linear regression model. These models are trained on different subsets of the data, and the final predictions are made by combining the predictions of all the models. The algorithm uses a technique called “bagging” to build these models, which stands for Bootstrap Aggregating and it’s a way to reduce the variance of a model.

In R, you can use the “Cubist” package to perform the cubist algorithm. 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 “cubist” function to fit a cubist 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 number of models to be used in the ensemble by setting the value of the “committees” parameter.

Once the model is fit, you can use the “predict” function to make predictions on new data. You can also use the “summary” function to get the summary of the model and evaluate its performance.

It’s important to note that cubist algorithm can be computationally intensive and it may take some time to run, depending on the size of the dataset. Also, like any other machine learning algorithm, it’s important to evaluate the performance of the model using evaluation metrics such as Mean Squared Error(MSE), Mean Absolute Error(MAE) and Root Mean Squared Error(RMSE) among others.

In summary, the Cubist algorithm is a type of non-linear regression that can be used to make predictions in complex datasets. It’s a hybrid algorithm that combines the strengths of both linear regression and decision trees. The “Cubist” package in R provides an easy and efficient way to fit cubist models and make predictions. However, it’s computationally intensive and it’s important to evaluate the performance of the model using evaluation metrics.

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 – cubist algorithm in R.