> 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/0.6-1/developer-guide/api/concrete.ml.sklearn.glm.md).

# concrete.ml.sklearn.glm.md

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L0)

## module `concrete.ml.sklearn.glm`

Implement sklearn's Generalized Linear Models (GLM).

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L134)

### class `PoissonRegressor`

A Poisson 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 PoissonRegressor please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.PoissonRegressor.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L155)

#### method `__init__`

```python
__init__(
    n_bits: 'Union[int, dict]' = 8,
    alpha: 'float' = 1.0,
    fit_intercept: 'bool' = True,
    max_iter: 'int' = 100,
    tol: 'float' = 0.0001,
    warm_start: 'bool' = False,
    verbose: 'int' = 0
)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L54)

#### method `post_processing`

```python
post_processing(
    y_preds: 'ndarray',
    already_dequantized: 'bool' = False
) → ndarray
```

Post-processing the predictions.

**Args:**

* `y_preds` (numpy.ndarray): The predictions to post-process.
* `already_dequantized` (bool): Whether the inputs were already dequantized or not. Default to False.

**Returns:**

* `numpy.ndarray`: The post-processed predictions.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L75)

#### method `predict`

```python
predict(X: 'ndarray', execute_in_fhe: 'bool' = False) → ndarray
```

Predict on user data.

Predict on user data using either the quantized clear model, implemented with tensors, or, if execute\_in\_fhe is set, using the compiled FHE circuit.

**Args:**

* `X` (numpy.ndarray): The input data.
* `execute_in_fhe` (bool): Whether to execute the inference in FHE. Default to False.

**Returns:**

* `numpy.ndarray`: The model's predictions.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L190)

### class `GammaRegressor`

A Gamma 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 GammaRegressor please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.GammaRegressor.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L211)

#### method `__init__`

```python
__init__(
    n_bits: 'Union[int, dict]' = 8,
    alpha: 'float' = 1.0,
    fit_intercept: 'bool' = True,
    max_iter: 'int' = 100,
    tol: 'float' = 0.0001,
    warm_start: 'bool' = False,
    verbose: 'int' = 0
)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L54)

#### method `post_processing`

```python
post_processing(
    y_preds: 'ndarray',
    already_dequantized: 'bool' = False
) → ndarray
```

Post-processing the predictions.

**Args:**

* `y_preds` (numpy.ndarray): The predictions to post-process.
* `already_dequantized` (bool): Whether the inputs were already dequantized or not. Default to False.

**Returns:**

* `numpy.ndarray`: The post-processed predictions.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L75)

#### method `predict`

```python
predict(X: 'ndarray', execute_in_fhe: 'bool' = False) → ndarray
```

Predict on user data.

Predict on user data using either the quantized clear model, implemented with tensors, or, if execute\_in\_fhe is set, using the compiled FHE circuit.

**Args:**

* `X` (numpy.ndarray): The input data.
* `execute_in_fhe` (bool): Whether to execute the inference in FHE. Default to False.

**Returns:**

* `numpy.ndarray`: The model's predictions.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L246)

### class `TweedieRegressor`

A Tweedie 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 TweedieRegressor please refer to the scikit-learn documentation: <https://scikit-learn.org/stable/modules/generated/sklearn.linear\\_model.TweedieRegressor.html>

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L267)

#### method `__init__`

```python
__init__(
    n_bits: 'Union[int, dict]' = 8,
    power: 'float' = 0.0,
    alpha: 'float' = 1.0,
    fit_intercept: 'bool' = True,
    link: 'str' = 'auto',
    max_iter: 'int' = 100,
    tol: 'float' = 0.0001,
    warm_start: 'bool' = False,
    verbose: 'int' = 0
)
```

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L54)

#### method `post_processing`

```python
post_processing(
    y_preds: 'ndarray',
    already_dequantized: 'bool' = False
) → ndarray
```

Post-processing the predictions.

**Args:**

* `y_preds` (numpy.ndarray): The predictions to post-process.
* `already_dequantized` (bool): Whether the inputs were already dequantized or not. Default to False.

**Returns:**

* `numpy.ndarray`: The post-processed predictions.

***

[![](https://img.shields.io/badge/-source-cccccc?style=flat-square)](https://github.com/zama-ai/concrete-ml/tree/release/0.6.x/src/concrete/ml/sklearn/glm.py#L75)

#### method `predict`

```python
predict(X: 'ndarray', execute_in_fhe: 'bool' = False) → ndarray
```

Predict on user data.

Predict on user data using either the quantized clear model, implemented with tensors, or, if execute\_in\_fhe is set, using the compiled FHE circuit.

**Args:**

* `X` (numpy.ndarray): The input data.
* `execute_in_fhe` (bool): Whether to execute the inference in FHE. Default to False.

**Returns:**

* `numpy.ndarray`: The model's predictions.


---

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