test: add failing test for large dataset performance (#420)

[ANAMASGARD]

• Test documents performance degradation with >10K observations
• check_model() currently takes 5+ minutes on 25K dataset
• Test expects completion in <30 seconds
• Initially FAILS as expected (proving bug exists)

This test will pass after implementing data sampling in plot.check_model().

In the Next Commit I will Push the Code with all the necessary Fixes .

Relates to #420

[gemini-code-assist[bot]]

Summary of Changes

Hello @ANAMASGARD, I'm Gemini Code Assist[^1]! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a comprehensive set of R tests designed to expose and quantify a significant performance bottleneck in the check_model() function when processing large datasets. By establishing a failing test case that demonstrates the current slow execution, it lays the groundwork for future performance improvements, specifically through the implementation of data sampling, while also ensuring that sampling does not negatively impact smaller datasets or visual accuracy.

Highlights

• Performance Degradation Test: Introduces a new test file (test-performance_large_datasets.R) to specifically highlight and reproduce a performance issue in check_model() with datasets exceeding 10,000 observations.
• Expected Failure & Future Fix: The test is designed to initially fail (taking over 5 minutes for a 25K dataset, expecting <30 seconds) to confirm the existence of the performance bug, with the expectation that it will pass once data sampling is implemented in plot.check_model().
• Sampling Behavior Tests: Includes additional tests to ensure that data sampling, once implemented, preserves visual fidelity for small datasets, significantly speeds up plotting for large datasets, and that medium datasets (below the sampling threshold) still perform reasonably well.

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