# Bonus Assignment 10#

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In this assignment, we go through the math and implement the general gradient boosting algorithm from scratch. The same class will implement a binary classifier that minimizes the logistic loss function and two regressors that minimize the mean squared error (MSE) and the root mean squared logarithmic error (RMSLE). This way, we will see that we can optimize arbitrary differentiable functions using gradient boosting and how this technique adapts to different contexts. Here is one of the questions:

Residuals at each gradient boosting iteration and the corresponding tree prediction: