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Fix a missing ending para and remove blank targets.
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@craigmoore1
craigmoore1 committedMay 3, 2024
commitafd202f92d435d25fe6652625d9fc32d24f62e76
2 changes: 1 addition & 1 deletionpgml-cms/docs/api/sql-extension/pgml.train/README.md
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Expand Up@@ -37,7 +37,7 @@ pgml.train(
| `task` | `'regression'` | The objective of the experiment: `regression`, `classification` or `cluster` |
| `relation_name` | `'public.search_logs'` | The Postgres table or view where the training data is stored or defined. |
| `y_column_name` | `'clicked'` | The name of the label (aka "target" or "unknown") column in the training table. |
| `algorithm` | `'xgboost'` | <p>The algorithm to train on the dataset. |
| `algorithm` | `'xgboost'` | <p>The algorithm to train on the dataset.</p> |
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We don't need the <p> if we're not nesting multiple links, but why remove the links?

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|`algorithm`|`'xgboost'`|<p>The algorithm to train on the dataset.</p>|
|`algorithm`|`'xgboost'`| The algorithm to train on the dataset.|

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The links 404'ed. Do the pages still exist?

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| `hyperparams` | `{ "n_estimators": 25 }` | The hyperparameters to pass to the algorithm for training, JSON formatted. |
| `search` | `grid` | If set, PostgresML will perform a hyperparameter search to find the best hyperparameters for the algorithm. See [hyperparameter-search.md](hyperparameter-search.md "mention") for details. |
| `search_params` | `{ "n_estimators": [5, 10, 25, 100] }` | Search parameters used in the hyperparameter search, using the scikit-learn notation, JSON formatted. |
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42 changes: 21 additions & 21 deletionspgml-cms/docs/api/sql-extension/pgml.train/classification.md
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Expand Up@@ -8,7 +8,7 @@ description: >-

## Example

This example trains models on the sklean digits dataset which is a copy of the test set of the [UCI ML hand-written digits datasets](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits target="_blank"). This demonstrates using a table with a single array feature column for classification. You could do something similar with a vector column.
This example trains models on the sklean digits dataset which is a copy of the test set of the [UCI ML hand-written digits datasets](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits). This demonstrates using a table with a single array feature column for classification. You could do something similar with a vector column.

```sql
-- load the sklearn digits dataset
Expand All@@ -33,16 +33,16 @@ LIMIT 10;

## Algorithms

We currently support classification algorithms from [scikit-learn](https://scikit-learn.org/ target="_blank"), [XGBoost](https://xgboost.readthedocs.io/ target="_blank"), [LightGBM](https://lightgbm.readthedocs.io/ target="_blank") and [Catboost](https://catboost.ai/ target="_blank").
We currently support classification algorithms from [scikit-learn](https://scikit-learn.org/), [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/) and [Catboost](https://catboost.ai/).

### Gradient Boosting

| Algorithm | Reference |
| ----------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `xgboost` | [XGBClassifier](https://xgboost.readthedocs.io/en/stable/python/python\_api.html#xgboost.XGBClassifier target="_blank") |
| `xgboost_random_forest` | [XGBRFClassifier](https://xgboost.readthedocs.io/en/stable/python/python\_api.html#xgboost.XGBRFClassifier target="_blank") |
| `lightgbm` | [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier target="_blank") |
| `catboost` | [CatBoostClassifier](https://catboost.ai/en/docs/concepts/python-reference\_catboostclassifier target="_blank") |
| `xgboost` | [XGBClassifier](https://xgboost.readthedocs.io/en/stable/python/python\_api.html#xgboost.XGBClassifier) |
| `xgboost_random_forest` | [XGBRFClassifier](https://xgboost.readthedocs.io/en/stable/python/python\_api.html#xgboost.XGBRFClassifier) |
| `lightgbm` | [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html#lightgbm.LGBMClassifier) |
| `catboost` | [CatBoostClassifier](https://catboost.ai/en/docs/concepts/python-reference\_catboostclassifier) |

#### Examples

Expand All@@ -57,12 +57,12 @@ SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'catboost', hyperpar

| Algorithm | Reference |
| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- |
| `ada_boost` | [AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html target="_blank") |
| `bagging` | [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html target="_blank") |
| `extra_trees` | [ExtraTreesClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html target="_blank") |
| `gradient_boosting_trees` | [GradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html target="_blank") |
| `random_forest` | [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html target="_blank") |
| `hist_gradient_boosting` | [HistGradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html target="_blank") |
| `ada_boost` | [AdaBoostClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) |
| `bagging` | [BaggingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingClassifier.html) |
| `extra_trees` | [ExtraTreesClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html) |
| `gradient_boosting_trees` | [GradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html) |
| `random_forest` | [RandomForestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html) |
| `hist_gradient_boosting` | [HistGradientBoostingClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html) |

#### Examples

Expand All@@ -79,9 +79,9 @@ SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'hist_gradient_boost

| Algorithm | Reference |
| ------------ | ----------------------------------------------------------------------------------------- |
| `svm` | [SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html target="_blank") |
| `nu_svm` | [NuSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html target="_blank") |
| `linear_svm` | [LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html target="_blank") |
| `svm` | [SVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html) |
| `nu_svm` | [NuSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html) |
| `linear_svm` | [LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) |

#### Examples

Expand All@@ -95,11 +95,11 @@ SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'linear_svm');

| Algorithm | Reference |
| ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------------- |
| `linear` | [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.LogisticRegression.html target="_blank") |
| `ridge` | [RidgeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.RidgeClassifier.html target="_blank") |
| `stochastic_gradient_descent` | [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.SGDClassifier.html target="_blank") |
| `perceptron` | [Perceptron](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.Perceptron.html target="_blank") |
| `passive_aggressive` | [PassiveAggressiveClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.PassiveAggressiveClassifier.html target="_blank") |
| `linear` | [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.LogisticRegression.html) |
| `ridge` | [RidgeClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.RidgeClassifier.html) |
| `stochastic_gradient_descent` | [SGDClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.SGDClassifier.html) |
| `perceptron` | [Perceptron](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.Perceptron.html) |
| `passive_aggressive` | [PassiveAggressiveClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear\_model.PassiveAggressiveClassifier.html) |

#### Examples

Expand All@@ -114,7 +114,7 @@ SELECT * FROM pgml.train('Handwritten Digits', algorithm => 'passive_aggressive'

| Algorithm | Reference |
| ------------------ | --------------------------------------------------------------------------------------------------------------------------------------- |
| `gaussian_process` | [GaussianProcessClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian\_process.GaussianProcessClassifier.html target="_blank") |
| `gaussian_process` | [GaussianProcessClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.gaussian\_process.GaussianProcessClassifier.html) |

#### Examples

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10 changes: 5 additions & 5 deletionspgml-cms/docs/api/sql-extension/pgml.train/clustering.md
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Expand Up@@ -4,7 +4,7 @@ Models can be trained using `pgml.train` on unlabeled data to identify groups wi

## Example

This example trains models on the sklearn digits dataset -- which is a copy of the test set of the [UCI ML hand-written digits datasets](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits target="_blank"). This demonstrates using a table with a single array feature column for clustering. You could do something similar with a vector column.
This example trains models on the sklearn digits dataset -- which is a copy of the test set of the [UCI ML hand-written digits datasets](https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits). This demonstrates using a table with a single array feature column for clustering. You could do something similar with a vector column.

```sql
SELECT pgml.load_dataset('digits');
Expand All@@ -31,10 +31,10 @@ All clustering algorithms implemented by PostgresML are online versions. You may

| Algorithm | Reference |
| ---------------------- | ----------------------------------------------------------------------------------------------------------------- |
| `affinity_propagation` | [AffinityPropagation](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html target="_blank") |
| `birch` | [Birch](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html target="_blank") |
| `kmeans` | [K-Means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html target="_blank") |
| `mini_batch_kmeans` | [MiniBatchKMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html target="_blank") |
| `affinity_propagation` | [AffinityPropagation](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html) |
| `birch` | [Birch](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.Birch.html) |
| `kmeans` | [K-Means](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html) |
| `mini_batch_kmeans` | [MiniBatchKMeans](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.MiniBatchKMeans.html) |

### Examples

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