> For the complete documentation index, see [llms.txt](https://docs.zama.org/concrete-ml/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zama.org/concrete-ml/0.4/developer-guide/api/concrete.ml.sklearn.linear_model.md).

# concrete.ml.sklearn.linear\_model

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L0)

## module `concrete.ml.sklearn.linear_model`

Implement sklearn linear model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L19)

### class `LinearRegression`

A linear regression model with FHE.

**Arguments:**

* **`n_bits`** (int): default is 2.
* **`use_sum_workaround`** (bool): indicate if the sum workaround should be used or not. This
* **`feature is experimental and should be used carefully. Important note`**: it only works for a LinearRegression model with N features, N a power of 2, for now. More information available in the QuantizedReduceSum operator. Default to False.

For more details on LinearRegression please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.LinearRegression.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L35)

#### method `__init__`

```python
__init__(
    n_bits=2,
    use_sum_workaround=False,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    n_jobs=None,
    positive=False
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

**Returns:**

* **`Circuit`**: the FHE circuit

***

**property input\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* **`onnx.ModelProto`**: the ONNX model

***

**property output\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property quantize\_input**

Get the input quantization function.

**Returns:**

* **`Callable`** : function that quantizes the input

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L57)

#### method `fit`

```python
fit(X, y: ndarray, *args, **kwargs) → Any
```

Fit the FHE linear model.

**Args:**

* **`X`** : training data By default, you should be able to pass: \* numpy arrays \* torch tensors \* pandas DataFrame or Series
* **`y`** (numpy.ndarray): The target data.
* **`*args`**: The arguments to pass to the sklearn linear model.
* **`**kwargs`**: The keyword arguments to pass to the sklearn linear model.

**Returns:** Any

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L134)

### class `ElasticNet`

An ElasticNet regression model with FHE.

**Arguments:**

* **`n_bits`** (int): default is 2.

For more details on ElasticNet please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.ElasticNet.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L146)

#### method `__init__`

```python
__init__(
    n_bits=2,
    alpha=1.0,
    l1_ratio=0.5,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    positive=False
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

**Returns:**

* **`Circuit`**: the FHE circuit

***

**property input\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* **`onnx.ModelProto`**: the ONNX model

***

**property output\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property quantize\_input**

Get the input quantization function.

**Returns:**

* **`Callable`** : function that quantizes the input

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L168)

### class `Lasso`

A Lasso regression model with FHE.

**Arguments:**

* **`n_bits`** (int): default is 2.

For more details on Lasso please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.Lasso.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L180)

#### method `__init__`

```python
__init__(
    n_bits=2,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    positive=False
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

**Returns:**

* **`Circuit`**: the FHE circuit

***

**property input\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* **`onnx.ModelProto`**: the ONNX model

***

**property output\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property quantize\_input**

Get the input quantization function.

**Returns:**

* **`Callable`** : function that quantizes the input

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L200)

### class `Ridge`

A Ridge regression model with FHE.

**Arguments:**

* **`n_bits`** (int): default is 2.

For more details on Ridge please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.Ridge.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L212)

#### method `__init__`

```python
__init__(
    n_bits=2,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    positive=False
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

**Returns:**

* **`Circuit`**: the FHE circuit

***

**property input\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* **`onnx.ModelProto`**: the ONNX model

***

**property output\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property quantize\_input**

Get the input quantization function.

**Returns:**

* **`Callable`** : function that quantizes the input

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L232)

### class `LogisticRegression`

A logistic regression model with FHE.

**Arguments:**

* **`n_bits`** (int): default is 2.

For more details on LogisticRegression please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.LogisticRegression.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/0.4.x/src/concrete/ml/sklearn/linear_model.py#L245)

#### method `__init__`

```python
__init__(
    n_bits=2,
    penalty='l2',
    dual=False,
    tol=0.0001,
    C=1.0,
    fit_intercept=True,
    intercept_scaling=1,
    class_weight=None,
    random_state=None,
    solver='lbfgs',
    max_iter=100,
    multi_class='auto',
    verbose=0,
    warm_start=False,
    n_jobs=None,
    l1_ratio=None
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

**Returns:**

* **`Circuit`**: the FHE circuit

***

**property input\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property onnx\_model**

Get the ONNX model.

.. # noqa: DAR201

**Returns:**

* **`onnx.ModelProto`**: the ONNX model

***

**property output\_quantizers**

Get the input quantizers.

**Returns:**

* **`List[QuantizedArray]`**: the input quantizers

***

**property quantize\_input**

Get the input quantization function.

**Returns:**

* **`Callable`** : function that quantizes the input


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