Concept Drift Handling
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In this problem, you will implement a concept drift handling system to adapt to changes in the distribution of a dataset over time. The system should be able to detect concept drift and update the model accordingly.
Example:
Suppose we have a model that predicts house prices based on features like number of bedrooms and square footage. We want to handle concept drift by updating the model's parameters.
Constraints:
The model's parameters should be updated based on the new data. The updated model should be able to make predictions on new data.
Test Cases
Test Case 1
Input:
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]Expected:
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]Test Case 2
Input:
[[10, 11, 12], [13, 14, 15], [16, 17, 18]]Expected:
[[10, 11, 12], [13, 14, 15], [16, 17, 18]]+ 3 hidden test cases