Updating root_mean_squared_log_error & mean_squared_log_error
#29686
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in _weight_boosting.py.
`ensemble/_weight_boosting.py` file, moving it below the `Examples` section for improved organization. Included an AdaBoost example reference within the DecisionTree class in the `tree/-class.py` file.
…sion Trees user guide. - Modified the doc-string wording in the `AdaBoostClassifier` class referencing to the aforementioned example.
…related to temperature scaling.
…argument. 2. Modified the `_temperature_scaling` function. The initial temperature is now 1.0, and the optimised temperature is in interval [1e-2, inf). 3. Revise doc-strings.
…py` for the first PR draft.
…_mean_squared_log_error` and `mean_squared_log_error` functions.
…_mean_squared_log_error` and `mean_squared_log_error` functions.
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Reference Issues/PRs
Towards #29678
What does this implement/fix? Explain your changes.
This PR updates the
root_mean_squared_log_errorand themean_squared_log_errorfunctions insklearn.metrics.The functions now check whether$y = \log(1 + x)$ , rather than $y = \log(x)$ , as was previously implemented.
y_trueandy_predare within the correct domain for the functionAny other comments?
Comments and feedback are more than welcome!