Fixes #13311 Adds SMAPE function to Loss functions

[VedanshTyagi]

Describe your change:

Fixes #13311
Adds the algorithm to calculate symmetric mean absolute percentage error in loss_functions.py file in machine_learning

• Add an algorithm?
• Fix a bug or typo in an existing algorithm?
• Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
• Documentation change?

Checklist:

• I have read CONTRIBUTING.md.
• This pull request is all my own work -- I have not plagiarized.
• I know that pull requests will not be merged if they fail the automated tests.
• This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
• All new Python files are placed inside an existing directory.
• All filenames are in all lowercase characters with no spaces or dashes.
• All functions and variable names follow Python naming conventions.
• All function parameters and return values are annotated with Python type hints.
• All functions have doctests that pass the automated testing.
• All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
• If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".

[priya-sundaram-dev]

Reviewed. Clean implementation that matches the formula in #13311. I verified the doctest: for [100,200,300,400] vs [110,190,310,420] the SMAPE (fractional form) is ≈0.05702, which matches. Good error path for length mismatch, sensible epsilon guard against a zero denominator, and CI is green.

Note for coordination: #13357 (MAPE) edits the same region of loss_functions.py, so whichever of the two merges second will need a trivial rebase. LGTM. 👍