Gradient Boosting Update

Apply one gradient-boosting update: add the weak learner's predictions, scaled by the learning rate (shrinkage), to the current ensemble predictions.

Implement solve(pred,weak_pred,learning_rate).

Returns. Return a float NumPy array $\widehat{y}+\eta h\backslash hat\; y+\backslash eta\backslash ,h$.

Examples

Example 1

Input:

solve([1.0, 2.0], [0.5, -1.0], 0.1)

Output:

[1.05, 1.9]

Example 2

Input:

solve([0.0, 0.0], [4.0, 8.0], 0.5)

Output:

[2.0, 4.0]