Update FAQ section of docs #997
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jeremyliweishih
approved these changes
Jul 30, 2020
| "\n", | ||
| "EvalML optimizes machine learning pipelines on [custom practical objectives](objectives.ipynb) instead of vague machine learning loss functions so that it will find the best pipelines for your specific needs. Furthermore, EvalML [pipelines](pipelines.ipynb) are able to take in all kinds of data (missing values, categorical, etc.) as long as the data are in a single table. EvalML also allows you to build your own pipelines with existing or custom components so you can have more control over the AutoML process. Moreover, EvalML also provides you with support in the form of [data checks](data_checks.ipynb) to ensure that you are aware of potential issues your data may cause with machine learning algorithms.\n", | ||
| "\n", | ||
| "**How does EvalML handle missing values?**\n", | ||
| "#### Q: How does EvalML handle missing values?\n", | ||
| "\n", | ||
| "EvalML contains imputation components in its pipelines so that missing values are taken care of. EvalML optimizes over different types of imputation to search for the best possible pipeline. You can find more information about components [here](components.ipynb) and in the API reference [here](../generated/evalml.pipelines.components.SimpleImputer.ipynb).\n", |
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Might need to change this to TypedImputer when the PR merges or just remove the SimpleImputer link.
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Ah good point... will link in the other PR
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Changes:
https://evalml.alteryx.com/en/ds_update_faq/user_guide/faq.html
The headers are too big. We have this problem on the index page too. Its an issue with the pandas style, need to add CSS for it another time.