ere's a pseudo code explanation of the Gradient Boosting Classifier algorithm:

Initialize the model with a constant value, usually the mean of the target variable.

For each iteration t in the number of estimators: a. Calculate the negative gradient of the loss function with respect to the current model predictions. b. Fit a new decision tree to the negative gradient as the target variable. c. Update the model predictions by adding the predictions from the new tree, scaled by a learning rate.

Return the final model.

The boosting algorithm iteratively improves the model by adding new decision trees that correct the errors made by the previous ones. 

The negative gradient of the loss function is used as the target variable for each new tree, which means that the tree learns to predict the residual errors of the previous model. 

The learning rate controls the contribution of each new tree to the final model.

Hope this helps!
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