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Jun 18, 2024
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fix 404s #1537

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8 changes: 4 additions & 4 deletions pgml-cms/docs/guides/supervised-learning.md
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### Training a Model

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.
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.

```postgresql
SELECT * FROM pgml.train(
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(1 row)
```

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.
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.

### Inspecting the results

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### Example

If you've already been through the [Training Overview](../../../docs/training/overview/), you can see the results of those efforts:
If you've executed the commands in this guide, you can see the results of those efforts:

```postgresql
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.

Take a look at [Deploying Models](../../../docs/predictions/deployments/) documentation for more details.
Take a look at [pgml.deploy](/docs/api/sql-extension/pgml.deploy) documentation for more details.

### Specific Models

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