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fix 404s (#1537)
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pgml-cms/docs/guides/supervised-learning.md

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### Training a Model
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Now that we've got data, we're ready to train a model using an algorithm. We'll start with the default `linear` algorithm to demonstrate the basics. See the [Algorithms](../../../docs/training/algorithm\_selection/) for a complete list of available algorithms.
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Now that we've got data, we're ready to train a model using an algorithm. We'll start with a classification task to demonstrate the basics. See [pgml.train](/docs/api/sql-extension/pgml.train/) for a complete list of available algorithms and tasks.
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```postgresql
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SELECT * FROM pgml.train(
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```
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The output gives us information about the training run, including the `deployed` status. This is great news indicating training has successfully reached a new high score for the project's key metric and our new model was automatically deployed as the one that will be used to make new predictions for the project. See [Deployments](../../../docs/predictions/deployments/) for a guide to managing the active model.
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The output gives us information about the training run, including the `deployed` status. This is great news indicating training has successfully reached a new high score for the project's key metric and our new model was automatically deployed as the one that will be used to make new predictions for the project.
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### Inspecting the results
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### Example
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If you've already been through the [Training Overview](../../../docs/training/overview/), you can see the results of those efforts:
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If you've executed the commands in this guide, you can see the results of those efforts:
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```postgresql
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SELECT
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PostgresML will automatically deploy a model only if it has better metrics than existing ones, so it's safe to experiment with different algorithms and hyperparameters.
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Take a look at [Deploying Models](../../../docs/predictions/deployments/) documentation for more details.
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Take a look at [pgml.deploy](/docs/api/sql-extension/pgml.deploy) documentation for more details.
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### Specific Models
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