.. _f-quantilemlpregressor: module ``mlmodel.quantile_mlpregressor`` ======================================== .. inheritance-diagram:: mlinsights.mlmodel.quantile_mlpregressor Short summary +++++++++++++ module ``mlinsights.mlmodel.quantile_mlpregressor`` Implements a quantile non-linear regression. :githublink:`%|py|6` Classes +++++++ +-------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | class | truncated documentation | +===================================================================================================================+====================================================================================================================================================================================+ | :class:`CustomizedMultilayerPerceptron ` | Customized MLP Perceptron based on `BaseMultilayerPerceptron `_. ... | +-------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | :class:`QuantileMLPRegressor ` | Quantile MLP Regression or neural networks regression trained with norm :epkg:`L1`. This class inherits from :epkg:`sklearn:neural_networks:MLPRegressor`. ... | +-------------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ Functions +++++++++ +--------------------------------------------------------------------------------+--------------------------------------------+ | function | truncated documentation | +================================================================================+============================================+ | :func:`absolute_loss ` | Computes the absolute loss for regression. | +--------------------------------------------------------------------------------+--------------------------------------------+ | :func:`float_sign ` | Returns 1 if *a > 0*, otherwise -1 | +--------------------------------------------------------------------------------+--------------------------------------------+ Properties ++++++++++ +--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------+ | property | truncated documentation | +==============================================================================================================+==================================================================================================================+ | :py:meth:`_repr_html_ ` | HTML representation of estimator. This is redundant with the logic of `_repr_mimebundle_`. The latter should ... | +--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_repr_html_ ` | HTML representation of estimator. This is redundant with the logic of `_repr_mimebundle_`. The latter should ... | +--------------------------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------+ Methods +++++++ +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | method | truncated documentation | +========================================================================================================================================+=======================================================================================================================+ | :py:meth:`__init__ ` | | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`__init__ ` | See :epkg:`sklearn:neural_networks:MLPRegressor` | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_backprop ` | Computes the MLP loss function and its corresponding derivatives with respect to each parameter: weights and bias ... | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_backprop ` | Computes the MLP loss function and its corresponding derivatives with respect to each parameter: weights and bias ... | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_get_loss_function ` | Returns the loss functions. | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_get_loss_function ` | Returns the loss functions. | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_modify_loss_derivatives ` | Modifies the loss derivatives. | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_modify_loss_derivatives ` | Modifies the loss derivatives. | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :py:meth:`_validate_input ` | | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :meth:`predict ` | Predicts using the multi-layer perceptron model. | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ | :meth:`score ` | Returns mean absolute error regression loss. | +----------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------+ Documentation +++++++++++++ .. automodule:: mlinsights.mlmodel.quantile_mlpregressor :members: :special-members: __init__ :show-inheritance: