mlinsights implements functions to get insights on machine learned models or various kind of transforms to help manipulating data in a single pipeline. It implements :class:`QuantileLinearRegression <mlinsights.mlmodel.quantile_regression.QuantileLinearRegression>` which trains a linear regression with :epkg:`L1` norm non-linear correlation based on decision trees, :class:`QuantileMLPRegressor <mlinsights.mlmodel.quantile_mlpregressor.QuantileMLPRegressor>` which is a modification of scikit-learn's :epkg:`MLPRegressor` which trains a multi-layer perceptron with :epkg:`L1` norm...
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:caption: Documentation
tutorial/index
api/index
auto_examples/index
i_ex
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.. toctree::
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license
CHANGELOGS
Short example:
.. runpython::
:showcode:
:warningout: FutureWarning
from sklearn.datasets import load_diabetes
from sklearn.linear_model import LinearRegression
from mlinsights.mlmodel import QuantileLinearRegression
data = load_diabetes()
X, y = data.data, data.target
clq = QuantileLinearRegression()
clq.fit(X, y)
print(clq.coef_)
clr = LinearRegression()
clr.fit(X, y)
print(clr.coef_)
This documentation was generated with :epkg:`scikit-learn` version...
.. runpython::
:showcode:
from sklearn import __version__
print(__version__)
Source are available at sdpython/mlinsights.