# SKLearn

This package provides a MLServer runtime compatible with Scikit-Learn.

## Usage

You can install the runtime, alongside `mlserver`, as:

```bash
pip install mlserver mlserver-sklearn
```

For further information on how to use MLServer with Scikit-Learn, you can check out this [worked out example](https://github.com/SeldonIO/MLServer/blob/master/docs/examples/sklearn/README.md).

## Content Types

If no [content type](https://github.com/SeldonIO/MLServer/blob/master/docs/user-guide/content-type/README.md) is present on the request or metadata, the Scikit-Learn runtime will try to decode the payload as a [NumPy Array](https://github.com/SeldonIO/MLServer/blob/master/docs/user-guide/content-type/README.md). To avoid this, either send a different content type explicitly, or define the correct one as part of your [model's metadata](https://github.com/SeldonIO/MLServer/blob/master/docs/reference/model-settings/README.md).

## Model Outputs

The Scikit-Learn inference runtime exposes a number of outputs depending on the model type. These outputs match to the `predict`, `predict_proba` and `transform` methods of the Scikit-Learn model.

| Output          | Returned By Default | Availability                                                                                                         |
| --------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------- |
| `predict`       | ✅                   | Available on most models, but not in [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html). |
| `predict_proba` | ❌                   | Only available on non-regressor models.                                                                              |
| `transform`     | ❌                   | Only available on [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html).                    |

By default, the runtime will only return the output of `predict`. However, you are able to control which outputs you want back through the `outputs` field of your {class}`InferenceRequest <mlserver.types.InferenceRequest>` payload.

For example, to only return the model's `predict_proba` output, you could define a payload such as:

```{code-block}
---
emphasize-lines: 10-12
---
{
  "inputs": [
    {
      "name": "my-input",
      "datatype": "INT32",
      "shape": [2, 2],
      "data": [1, 2, 3, 4]
    }
  ],
  "outputs": [
    { "name": "predict_proba" }
  ]
}
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.seldon.ai/mlserver/runtimes/sklearn.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.