Bias–variance decomposition helps you explain why a machine learning algorithm makes prediction errors by splitting expected error into parts from underfitting and overfitting. In this question, you’ll compute bias, variance, and noise from repeated model predictions on the same inputs.

Requirements

Implement the function

Rules:

• Use the squared error decomposition: $\mathbb{E}[(\hat{f}(x)-y)^2] = \text{Bias}(\hat{f}(x))^2 + \text{Var}(\hat{f}(x)) + \sigma^2.$
• Treat each row in predictions as an independent run (e.g., different training set, bootstrap sample, or random seed) of the same ml_algorithm.
• Return global bias_squared, variance, and noise as averages across all n inputs.
• Use only NumPy (and Python built-ins); don’t use any ML libraries.

Example

Output: