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Fix typos and improve docs

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Andras Schmelczer 2022-07-15 20:03:13 +02:00
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docs/how-to-guides/handle-training-data.md
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@ -7,9 +7,9 @@ In order to simplify your training data management, `great-ai` provide two compl
At the start of your experiments' first iteration, after you've gathered suitable samples for training, you can call [great_ai.add_ground_truth][]. This automatically stores a timestamp and also allows you to assign tags to the data. Using these attributes, [great_ai.query_ground_truth][] can be called to get a filtered view of the training data.
!!! important "Train-test-validation splits"
It is a best-practice to lock-away a test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it.
It is a best-practice to lock-away the test split of your data that is only used for the final quality assessment. This prevents you from accidentally training on it, or inadvertently tuning the model to have the highest accuracy metrics on the test split. This, of course, may lead to dubious results, hence, care must be taken to avoid it.
With [great_ai.add_ground_truth][], there is an option to tag the samples with `train`, `test`, and `validation` randomly, following a predefined distribution. This happens as soon as they're written in the database. Later on, these can be queried by providing the name of the appropriate tags.
With [great_ai.add_ground_truth][], there is an option to tag the samples with `train`, `test`, and `validation` randomly, following a predefined distribution. This happens as soon as they're written in the database. Later, these can be queried by providing the name of the appropriate tags.
The nice thing about this is that the 'input-expected output' pairs are stored as traces. Thus, they behave exactly like regular prediction traces.
@ -52,7 +52,7 @@ add_ground_truth(
1. Expected output. This can be also accessed through the `.output` property.
2. The input value is stored here.
3. Notice how `ground_truth` always included as a tag when using [great_ai.add_ground_truth][].
3. Notice how `ground_truth` is always included as a tag when using [great_ai.add_ground_truth][].
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