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# Classification in R – Bagging CART in R

Classification is a type of supervised machine learning that is used to predict the class or category of a new observation based on the values of its predictors. One popular method of classification is using bagging (also known as bootstrap aggregating) with Classification and Regression Trees (CART) algorithm.

Bagging is a technique that combines multiple decision tree models to improve the stability and accuracy of the predictions. It works by creating multiple subsets of the data using a technique called bootstrapping, and then training a decision tree model on each subset. The final predictions are made by averaging the predictions from all the decision tree models.

Classification and Regression Trees (CART) is a decision tree-based algorithm that can be used for both classification and regression tasks. It works by recursively splitting the data into subsets based on the values of the predictor variables, and then using these subsets to make predictions.

In R, there are several packages available for building bagging CART models, such as the ‘ipred’ and ‘rpart’ packages. These packages provide functions for creating and training bagging CART models, as well as functions for evaluating the performance of the model.

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

- 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.
- Define the model: The next step is to define the structure of the model, including the number of decision trees and the number of features to consider when creating each tree.
- Train the model: The model is trained using the prepared data. The model will create multiple decision trees using random subsets of the data and features with bagging and using CART algorithm.
- Evaluate the model: The model’s performance is evaluated using various metrics such as accuracy, precision, recall, and F1 score.
- Make predictions: Once the model is trained and evaluated, it can be used to make predictions on new data.

By using Bagging and CART algorithm in R, you can improve the stability and accuracy of the predictions. This is particularly useful when you have a large amount of data and complex relationships between variables. Bagging CART algorithm is considered as one of the most accurate and robust algorithm for classification and it can handle large amount of data and missing values 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: Classification in R – Bagging CART in R.

## Classification in R – Bagging CART in R

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.

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