Model Monitoring
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In this problem, you will implement a model monitoring system to track the performance of a machine learning model in production. The system should be able to calculate the mean absolute error (MAE) and mean squared error (MSE) of the model's predictions.
Example:
Suppose we have a model that predicts house prices based on features like number of bedrooms and square footage. We want to monitor its performance on a test dataset.
Constraints:
The model's predictions should be compared to the actual values in the test dataset. The MAE and MSE should be calculated over all data points.
Test Cases
Test Case 1
Input:
[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]Expected:
[1.0, 2.25]Test Case 2
Input:
[[7.0, 8.0], [9.0, 10.0], [11.0, 12.0]]Expected:
[1.0, 2.25]+ 3 hidden test cases