Certainly! Here's a simplified pseudo code for the Gradient Boosting Classifier algorithm:

1. Initialize the dataset and preprocessing steps (e.g., feature scaling, encoding, etc.)
2. Specify the number of iterations (trees) `n_trees` and the learning rate `eta`
3. Initialize a base model (e.g., a constant prediction)
4. For each iteration `i` in `1` to `n_trees`:
    a. Calculate the residuals (true values minus predictions) for each instance in the dataset
    b. Fit a new decision tree to the residuals
        i. Choose a feature to split the data on, and a threshold value
        ii. Calculate the best split by minimizing the squared residuals for each region
        iii. Build the tree by recursively splitting the data, using a specified maximum depth
    c. Update the base model by adding the weighted decision tree output multiplied by the learning rate `eta`
5. Make predictions using the final ensemble model (sum of the base model and all weighted decision trees)

This pseudo code outlines the core steps of the Gradient Boosting Classifier algorithm. The algorithm iteratively builds an ensemble of decision trees, each of which attempts to correct the errors made by the previous trees. The learning rate eta determines how much each new tree contributes to the overall model. By combining multiple weak learners, the Gradient Boosting Classifier is able to achieve strong predictive performance on a wide range of problems, such as predicting loan defaults.
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