Gradient Boosting
In this problem, you will implement a basic gradient boosting algorithm using NumPy. Gradient boosting is an ensemble method that combines multiple weak models to create a strong predictive model.
**Example:** Suppose we have a dataset of patients with features such as age, sex, and disease status. We can use gradient boosting to predict the disease status based on the features.
**Constraints:** You must use NumPy to perform the calculations.
Write a function that takes in the dataset and returns the predicted disease status.
Test Cases
Test Case 1
Input:
[[1, 0, 0], [2, 1, 1], [3, 0, 1]]Expected:
[0.0, 0.0, 0.0]Test Case 2
Input:
[[4, 1, 1], [5, 0, 1], [6, 1, 0]]Expected:
[0.0, 0.0, 0.0]Test Case 3
Input:
[[7, 0, 1], [8, 1, 0], [9, 0, 1]]Expected:
undefinedTest Case 4
Input:
[[10, 1, 1], [11, 0, 0], [12, 1, 1]]Expected:
undefinedTest Case 5
Input:
[[13, 0, 0], [14, 1, 1], [15, 0, 1]]Expected:
undefined