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---
title: SklearnSVM
description: API reference for qiskit.aqua.algorithms.SklearnSVM
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.aqua.algorithms.SklearnSVM
---
# qiskit.aqua.algorithms.SklearnSVM
<Class id="qiskit.aqua.algorithms.SklearnSVM" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/aqua/algorithms/classifiers/sklearn_svm/sklearn_svm.py" signature="SklearnSVM(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)" modifiers="class">
The Sklearn SVM algorithm (classical).
This scikit-learn based SVM algorithm uses a classical approach to experiment with feature map classification problems. See also the quantum classifier [`QSVM`](qiskit.aqua.algorithms.QSVM "qiskit.aqua.algorithms.QSVM").
Internally, this algorithm will run the binary classification or multiclass classification based on how many classes the data has. If the data has more than 2 classes then a *multiclass\_extension* is required to be supplied. Aqua provides several [`multiclass_extensions`](qiskit.aqua.components.multiclass_extensions#module-qiskit.aqua.components.multiclass_extensions "qiskit.aqua.components.multiclass_extensions").
**Parameters**
* **training\_dataset** (`Dict`\[`str`, `ndarray`]) – Training dataset.
* **test\_dataset** (`Optional`\[`Dict`\[`str`, `ndarray`]]) – Testing dataset.
* **datapoints** (`Optional`\[`ndarray`]) – Prediction dataset.
* **gamma** (`Optional`\[`int`]) – Used as input for sklearn rbf\_kernel which is used internally. See [sklearn.metrics.pairwise.rbf\_kernel](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html) for more information about gamma.
* **multiclass\_extension** (`Optional`\[`MulticlassExtension`]) – If number of classes is greater than 2 then a multiclass scheme must be supplied, in the form of a multiclass extension.
**Raises**
[**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – Multiclass extension not supplied when number of classes > 2
### \_\_init\_\_
<Function id="qiskit.aqua.algorithms.SklearnSVM.__init__" signature="__init__(training_dataset, test_dataset=None, datapoints=None, gamma=None, multiclass_extension=None)">
**Parameters**
* **training\_dataset** (`Dict`\[`str`, `ndarray`]) – Training dataset.
* **test\_dataset** (`Optional`\[`Dict`\[`str`, `ndarray`]]) – Testing dataset.
* **datapoints** (`Optional`\[`ndarray`]) – Prediction dataset.
* **gamma** (`Optional`\[`int`]) –
Used as input for sklearn rbf\_kernel which is used internally. See [sklearn.metrics.pairwise.rbf\_kernel](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.rbf_kernel.html) for more information about gamma.
* **multiclass\_extension** (`Optional`\[`MulticlassExtension`]) – If number of classes is greater than 2 then a multiclass scheme must be supplied, in the form of a multiclass extension.
**Raises**
[**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – Multiclass extension not supplied when number of classes > 2
</Function>
## Methods
| | |
| ---------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------- |
| [`__init__`](#qiskit.aqua.algorithms.SklearnSVM.__init__ "qiskit.aqua.algorithms.SklearnSVM.__init__")(training\_dataset\[, test\_dataset, …]) | **type training\_dataset**`Dict`\[`str`, `ndarray`] |
| [`load_model`](#qiskit.aqua.algorithms.SklearnSVM.load_model "qiskit.aqua.algorithms.SklearnSVM.load_model")(file\_path) | Load a model from a file path. |
| [`predict`](#qiskit.aqua.algorithms.SklearnSVM.predict "qiskit.aqua.algorithms.SklearnSVM.predict")(data) | Predict using the SVM |
| [`run`](#qiskit.aqua.algorithms.SklearnSVM.run "qiskit.aqua.algorithms.SklearnSVM.run")() | Execute the classical algorithm. |
| [`save_model`](#qiskit.aqua.algorithms.SklearnSVM.save_model "qiskit.aqua.algorithms.SklearnSVM.save_model")(file\_path) | Save the model to a file path. |
| [`test`](#qiskit.aqua.algorithms.SklearnSVM.test "qiskit.aqua.algorithms.SklearnSVM.test")(data, labels) | Test the SVM |
| [`train`](#qiskit.aqua.algorithms.SklearnSVM.train "qiskit.aqua.algorithms.SklearnSVM.train")(data, labels) | Train the SVM |
## Attributes
| | |
| ------------------------------------------------------------------------------------------------------------------------ | ---------------------- |
| [`class_to_label`](#qiskit.aqua.algorithms.SklearnSVM.class_to_label "qiskit.aqua.algorithms.SklearnSVM.class_to_label") | returns class to label |
| [`label_to_class`](#qiskit.aqua.algorithms.SklearnSVM.label_to_class "qiskit.aqua.algorithms.SklearnSVM.label_to_class") | returns label to class |
| [`random`](#qiskit.aqua.algorithms.SklearnSVM.random "qiskit.aqua.algorithms.SklearnSVM.random") | Return a numpy random. |
| [`ret`](#qiskit.aqua.algorithms.SklearnSVM.ret "qiskit.aqua.algorithms.SklearnSVM.ret") | returns result |
### class\_to\_label
<Attribute id="qiskit.aqua.algorithms.SklearnSVM.class_to_label">
returns class to label
</Attribute>
### label\_to\_class
<Attribute id="qiskit.aqua.algorithms.SklearnSVM.label_to_class">
returns label to class
</Attribute>
### load\_model
<Function id="qiskit.aqua.algorithms.SklearnSVM.load_model" signature="load_model(file_path)">
Load a model from a file path.
**Parameters**
**file\_path** (*str*) – the path of the saved model.
</Function>
### predict
<Function id="qiskit.aqua.algorithms.SklearnSVM.predict" signature="predict(data)">
Predict using the SVM
**Parameters**
**data** (*numpy.ndarray*) – NxD array, where N is the number of data, D is the feature dimension.
**Returns**
predicted labels, Nx1 array
**Return type**
numpy.ndarray
</Function>
### random
<Attribute id="qiskit.aqua.algorithms.SklearnSVM.random">
Return a numpy random.
</Attribute>
### ret
<Attribute id="qiskit.aqua.algorithms.SklearnSVM.ret">
returns result
</Attribute>
### run
<Function id="qiskit.aqua.algorithms.SklearnSVM.run" signature="run()">
Execute the classical algorithm.
**Returns**
results of an algorithm.
**Return type**
dict
</Function>
### save\_model
<Function id="qiskit.aqua.algorithms.SklearnSVM.save_model" signature="save_model(file_path)">
Save the model to a file path.
**Parameters**
**file\_path** (*str*) – a path to save the model.
</Function>
### test
<Function id="qiskit.aqua.algorithms.SklearnSVM.test" signature="test(data, labels)">
Test the SVM
**Parameters**
* **data** (*numpy.ndarray*) – NxD array, where N is the number of data, D is the feature dimension.
* **labels** (*numpy.ndarray*) – Nx1 array, where N is the number of data
**Returns**
accuracy
**Return type**
float
</Function>
### train
<Function id="qiskit.aqua.algorithms.SklearnSVM.train" signature="train(data, labels)">
Train the SVM
**Parameters**
* **data** (*numpy.ndarray*) – NxD array, where N is the number of data, D is the feature dimension.
* **labels** (*numpy.ndarray*) – Nx1 array, where N is the number of data
</Function>
</Class>
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