# concrete.ml.sklearn.linear\_model.md

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.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/1.1.x/src/concrete/ml/sklearn/linear_model.py#L10)

### class `LinearRegression`

A linear regression model with FHE.

**Parameters:**

* `n_bits` (int, Dict\[str, int]): Number of bits to quantize the model. If an int is passed for n\_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op\_inputs" and "op\_weights" as keys with corresponding number of quantization bits so that: - op\_inputs : number of bits to quantize the input values - op\_weights: number of bits to quantize the learned parameters Default to 8.

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/1.1.x/src/concrete/ml/sklearn/linear_model.py#L30)

#### method `__init__`

```python
__init__(
    n_bits=8,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    n_jobs=None,
    positive=False
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: <https://docs.zama.ai/concrete/developer/terminology\\_and\\_structure#terminology> Is None if the model is not fitted.

**Returns:**

* `Circuit`: The FHE circuit.

***

**property is\_compiled**

Indicate if the model is compiled.

**Returns:**

* `bool`: If the model is compiled.

***

**property is\_fitted**

Indicate if the model is fitted.

**Returns:**

* `bool`: If the model is fitted.

***

**property onnx\_model**

Get the ONNX model.

Is None if the model is not fitted.

**Returns:**

* `onnx.ModelProto`: The ONNX model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L48)

#### method `dump_dict`

```python
dump_dict() → Dict[str, Any]
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L75)

#### classmethod `load_dict`

```python
load_dict(metadata: Dict)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L103)

### class `ElasticNet`

An ElasticNet regression model with FHE.

**Parameters:**

* `n_bits` (int, Dict\[str, int]): Number of bits to quantize the model. If an int is passed for n\_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op\_inputs" and "op\_weights" as keys with corresponding number of quantization bits so that: - op\_inputs : number of bits to quantize the input values - op\_weights: number of bits to quantize the learned parameters Default to 8.

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/1.1.x/src/concrete/ml/sklearn/linear_model.py#L123)

#### method `__init__`

```python
__init__(
    n_bits=8,
    alpha=1.0,
    l1_ratio=0.5,
    fit_intercept=True,
    normalize='deprecated',
    precompute=False,
    max_iter=1000,
    copy_X=True,
    tol=0.0001,
    warm_start=False,
    positive=False,
    random_state=None,
    selection='cyclic'
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: <https://docs.zama.ai/concrete/developer/terminology\\_and\\_structure#terminology> Is None if the model is not fitted.

**Returns:**

* `Circuit`: The FHE circuit.

***

**property is\_compiled**

Indicate if the model is compiled.

**Returns:**

* `bool`: If the model is compiled.

***

**property is\_fitted**

Indicate if the model is fitted.

**Returns:**

* `bool`: If the model is fitted.

***

**property onnx\_model**

Get the ONNX model.

Is None if the model is not fitted.

**Returns:**

* `onnx.ModelProto`: The ONNX model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L155)

#### method `dump_dict`

```python
dump_dict() → Dict[str, Any]
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L189)

#### classmethod `load_dict`

```python
load_dict(metadata: Dict)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L225)

### class `Lasso`

A Lasso regression model with FHE.

**Parameters:**

* `n_bits` (int, Dict\[str, int]): Number of bits to quantize the model. If an int is passed for n\_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op\_inputs" and "op\_weights" as keys with corresponding number of quantization bits so that: - op\_inputs : number of bits to quantize the input values - op\_weights: number of bits to quantize the learned parameters Default to 8.

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/1.1.x/src/concrete/ml/sklearn/linear_model.py#L245)

#### method `__init__`

```python
__init__(
    n_bits=8,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    precompute=False,
    copy_X=True,
    max_iter=1000,
    tol=0.0001,
    warm_start=False,
    positive=False,
    random_state=None,
    selection='cyclic'
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: <https://docs.zama.ai/concrete/developer/terminology\\_and\\_structure#terminology> Is None if the model is not fitted.

**Returns:**

* `Circuit`: The FHE circuit.

***

**property is\_compiled**

Indicate if the model is compiled.

**Returns:**

* `bool`: If the model is compiled.

***

**property is\_fitted**

Indicate if the model is fitted.

**Returns:**

* `bool`: If the model is fitted.

***

**property onnx\_model**

Get the ONNX model.

Is None if the model is not fitted.

**Returns:**

* `onnx.ModelProto`: The ONNX model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L275)

#### method `dump_dict`

```python
dump_dict() → Dict[str, Any]
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L308)

#### classmethod `load_dict`

```python
load_dict(metadata: Dict)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L343)

### class `Ridge`

A Ridge regression model with FHE.

**Parameters:**

* `n_bits` (int, Dict\[str, int]): Number of bits to quantize the model. If an int is passed for n\_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op\_inputs" and "op\_weights" as keys with corresponding number of quantization bits so that: - op\_inputs : number of bits to quantize the input values - op\_weights: number of bits to quantize the learned parameters Default to 8.

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/1.1.x/src/concrete/ml/sklearn/linear_model.py#L363)

#### method `__init__`

```python
__init__(
    n_bits=8,
    alpha: float = 1.0,
    fit_intercept=True,
    normalize='deprecated',
    copy_X=True,
    max_iter=None,
    tol=0.001,
    solver='auto',
    positive=False,
    random_state=None
)
```

***

**property fhe\_circuit**

Get the FHE circuit.

The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: <https://docs.zama.ai/concrete/developer/terminology\\_and\\_structure#terminology> Is None if the model is not fitted.

**Returns:**

* `Circuit`: The FHE circuit.

***

**property is\_compiled**

Indicate if the model is compiled.

**Returns:**

* `bool`: If the model is compiled.

***

**property is\_fitted**

Indicate if the model is fitted.

**Returns:**

* `bool`: If the model is fitted.

***

**property onnx\_model**

Get the ONNX model.

Is None if the model is not fitted.

**Returns:**

* `onnx.ModelProto`: The ONNX model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L389)

#### method `dump_dict`

```python
dump_dict() → Dict[str, Any]
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L420)

#### classmethod `load_dict`

```python
load_dict(metadata: Dict)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L452)

### class `LogisticRegression`

A logistic regression model with FHE.

**Parameters:**

* `n_bits` (int, Dict\[str, int]): Number of bits to quantize the model. If an int is passed for n\_bits, the value will be used for quantizing inputs and weights. If a dict is passed, then it should contain "op\_inputs" and "op\_weights" as keys with corresponding number of quantization bits so that: - op\_inputs : number of bits to quantize the input values - op\_weights: number of bits to quantize the learned parameters Default to 8.

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/1.1.x/src/concrete/ml/sklearn/linear_model.py#L472)

#### method `__init__`

```python
__init__(
    n_bits=8,
    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.

The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: <https://docs.zama.ai/concrete/developer/terminology\\_and\\_structure#terminology> Is None if the model is not fitted.

**Returns:**

* `Circuit`: The FHE circuit.

***

**property is\_compiled**

Indicate if the model is compiled.

**Returns:**

* `bool`: If the model is compiled.

***

**property is\_fitted**

Indicate if the model is fitted.

**Returns:**

* `bool`: If the model is fitted.

***

**property onnx\_model**

Get the ONNX model.

Is None if the model is not fitted.

**Returns:**

* `onnx.ModelProto`: The ONNX model.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L510)

#### method `dump_dict`

```python
dump_dict() → Dict[str, Any]
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/blob/release/1.1.x/src/concrete/ml/sklearn/linear_model.py#L551)

#### classmethod `load_dict`

```python
load_dict(metadata: Dict)
```


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