Non-Linear Regression in R – cubist algorithm in R

Hits: 115

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.

Non-Linear Regression in R – cubist algorithm in R

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!