Now that we've got data, we're ready to train a model using an algorithm. We'll start withthe default`linear` algorithmto demonstrate the basics. Seethe[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 witha classification taskto demonstrate the basics. See[pgml.train](/docs/api/sql-extension/pgml.train/) for a complete list of available algorithms and tasks.

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.

If you'vealready been through the[Training Overview](../../../docs/training/overview/), you can see the results of those efforts:

If you'veexecuted the commands in this guide, you can see the results of those efforts:

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.