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机器学习代写 | Machine Learning Group Project 2

本次美国代写是机器学习相关的一个作业

Overview:https://clear.ml/docs/latest/docs (Links to an external site.)

Getting started:https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps (Links to an external site.)
https://www.youtube.com/watch?v=lD84X0_0TBE (Links to an external site.)

Steps:

    1. Each team is expected to select a machine learning dataset – call this clean dataset.
    2. Change the dataset to introduce incorrect data/outliers/gaps – call this dirty dataset.
    3. Download clear.ml using instructions above.
    4. Start using clear.ml with the dirty dataset and version the dataset.
    5. Run EDA (exploratory data analysis) of the dirty dataset – submit the plots.
    6. Use the dirty dataset to build a baseline machine learning model.
    7. Run metrics for this model.
    8. Update the dataset version to go from dirty to clean dataset.
    9. Run EDA (exploratory data analysis) of the clean dataset – submit the plots.
    10. Build a machine learning model with this “new” clean dataset.
    11. Run metrics for this model.
    12. Compare and comment on the accuracy/metrics of the models using the dirty and clean datasets.
    13. Submit the models and the comparison from step 12.

Additional resources on clear.ml (Links to an external site.):

Clearml GitHub:
https://github.com/allegroai/clearml (Links to an external site.)
https://clear.ml (Links to an external site.)

Getting started:
https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps (Links to an external site.)

Simple sklearn training example:
https://clear.ml/docs/latest/docs/guides/frameworks/scikit-learn/sklearn_joblib_example (Links to an external site.)

UI walk through:
https://clear.ml/docs/latest/docs/webapp/webapp_overview


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