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Update FAQ section of docs #997

Merged
merged 3 commits into from
Jul 30, 2020
Merged

Update FAQ section of docs #997

merged 3 commits into from
Jul 30, 2020

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dsherry
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@dsherry dsherry commented Jul 30, 2020

Changes:

  • Reformat question style
  • Delete empty cell

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.

@dsherry dsherry added the documentation Improvements or additions to documentation label Jul 30, 2020
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codecov bot commented Jul 30, 2020

Codecov Report

Merging #997 into main will not change coverage.
The diff coverage is n/a.

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@@           Coverage Diff           @@
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  Files         179      179           
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  Hits         9411     9411           
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@dsherry dsherry marked this pull request as ready for review July 30, 2020 19:41
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@jeremyliweishih jeremyliweishih left a comment

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LGTM - other than 1 comment about SimpleImputer. Rest of the content looks good.

"\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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