Non-Linear Regression in R – gradient boosted machine in R

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Non-Linear Regression in R – gradient boosted machine 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 gradient boosted machines (GBM).

A gradient boosting machine is a type of machine learning algorithm that is used for both classification and regression tasks. It is an ensemble method, which means it combines the predictions of multiple models to create a more accurate final prediction.

In R, there are several packages available for building gradient boosting machines, such as the ‘xgboost’ and ‘gbm’ packages. These packages provide functions for creating and training gradient boosting machines, as well as functions for evaluating the performance of the model.

The process of building a gradient boosting machine 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 trees and the depth of the trees.
  3. Train the model: The model is trained using the prepared data. The model will adjust the weights of the trees to minimize the error between the predicted and actual values.
  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 gradient boosting machines in R, you can model non-linear relationship and get accurate predictions. It has proven to be a powerful method for dealing with complex data and non-linear relationships between variables. It’s also fast for large data sets and can handle categorical variables as well.

 

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 – gradient boosted machine in R.



Non-Linear Regression in R – gradient boosted machine in R

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