# Scikit-Learn runtime for MLServer
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](../../docs/examples/sklearn/README.md).
## Content Types
If no [content type](../../docs/user-guide/content-type) is present on the
request or metadata, the Scikit-Learn runtime will try to decode the payload as
a [NumPy Array](../../docs/user-guide/content-type).
To avoid this, either send a different content type explicitly, or define the
correct one as part of your [model's
metadata](../../docs/reference/model-settings).
## 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} json
---
emphasize-lines: 10-12
---
{
"inputs": [
{
"name": "my-input",
"datatype": "INT32",
"shape": [2, 2],
"data": [1, 2, 3, 4]
}
],
"outputs": [
{ "name": "predict_proba" }
]
}
```
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"description": "# Scikit-Learn runtime for MLServer\n\nThis package provides a MLServer runtime compatible with Scikit-Learn.\n\n## Usage\n\nYou can install the runtime, alongside `mlserver`, as:\n\n```bash\npip install mlserver mlserver-sklearn\n```\n\nFor further information on how to use MLServer with Scikit-Learn, you can check\nout this [worked out example](../../docs/examples/sklearn/README.md).\n\n## Content Types\n\nIf no [content type](../../docs/user-guide/content-type) is present on the\nrequest or metadata, the Scikit-Learn runtime will try to decode the payload as\na [NumPy Array](../../docs/user-guide/content-type).\nTo avoid this, either send a different content type explicitly, or define the\ncorrect one as part of your [model's\nmetadata](../../docs/reference/model-settings).\n\n## Model Outputs\n\nThe Scikit-Learn inference runtime exposes a number of outputs depending on the\nmodel type.\nThese outputs match to the `predict`, `predict_proba` and `transform` methods\nof the Scikit-Learn model.\n\n| Output | Returned By Default | Availability |\n| --------------- | ------------------- | -------------------------------------------------------------------------------------------------------------------- |\n| `predict` | \u2705 | Available on most models, but not in [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html). |\n| `predict_proba` | \u274c | Only available on non-regressor models. |\n| `transform` | \u274c | Only available on [Scikit-Learn pipelines](https://scikit-learn.org/stable/modules/compose.html). |\n\nBy default, the runtime will only return the output of `predict`.\nHowever, you are able to control which outputs you want back through the\n`outputs` field of your {class}`InferenceRequest\n<mlserver.types.InferenceRequest>` payload.\n\nFor example, to only return the model's `predict_proba` output, you could\ndefine a payload such as:\n\n```{code-block} json\n---\nemphasize-lines: 10-12\n---\n{\n \"inputs\": [\n {\n \"name\": \"my-input\",\n \"datatype\": \"INT32\",\n \"shape\": [2, 2],\n \"data\": [1, 2, 3, 4]\n }\n ],\n \"outputs\": [\n { \"name\": \"predict_proba\" }\n ]\n}\n```\n",
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