# Classification in R – gradient boosted machine 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 gradient boosted machine (GBM).

GBM is an ensemble method that combines multiple decision tree models to improve the accuracy of predictions. It works by training a series of decision tree models, where each tree is trained to correct the errors of the previous tree. The final predictions are made by combining the predictions from all the decision trees. GBM algorithm uses gradient descent optimization technique to minimize the loss function and improve the accuracy of the model.

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

The process of building a GBM 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, the learning rate and the parameters of the decision tree.

**Train the model:** The model is trained using the prepared data. The model will create multiple decision trees by iteratively correcting the errors of the previous tree, using gradient descent optimization technique.

**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.

GBM is considered as one of the most accurate and robust algorithm for classification. It’s particularly useful when you have large amount of data, complex non-linear relationships.

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 – gradient boosted machine in R.

## Classification in R – gradient boosted machine in R

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