8000 DOC Added example comparing L1-based models to Lasso, ElasticNet and … · supersom/scikit-learn@c47dcbb · GitHub
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DOC Added example comparing L1-based models to Lasso, ElasticNet and ARD docstrings and demonstrating ARD to ARD docstring [as in PR scikit-learn#30587]
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‎sklearn/linear_model/_bayes.py

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@@ -590,9 +590,12 @@ class ARDRegression(RegressorMixin, LinearModel):
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>>> clf.predict([[1, 1]])
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array([1.])
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- :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` demonstrates ARD Regression.
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- :ref:`sphx_glr_auto_examples_linear_model_plot_ard.py` demonstrates ARD
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Regression.
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- :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` showcases ARD Regression alongside Lasso and Elastic-Net for sparse, correlated signals, in the presence of noise.
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- :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
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showcases ARD Regression alongside Lasso and Elastic-Net for sparse,
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correlated signals, in the presence of noise.
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"""
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_parameter_constraints: dict = {

‎sklearn/linear_model/_coordinate_descent.py

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@@ -876,7 +876,9 @@ class ElasticNet(MultiOutputMixin, RegressorMixin, LinearModel):
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>>> print(regr.predict([[0, 0]]))
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[1.451]
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- :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` showcases ElasticNet alongside Lasso and ARD Regression for sparse signal recovery in the presence of noise and feature correlation.
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- :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
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showcases ElasticNet alongside Lasso and ARD Regression for sparse signal
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recovery in the presence of noise and feature correlation.
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"""
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# "check_input" is used for optimisation and isn't something to be passed
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>>> print(clf.intercept_)
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0.15
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- :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py` compares Lasso with other L1-based regression models (ElasticNet and ARD Regression) for sparse signal recovery in the presence of noise and feature correlation.
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- :ref:`sphx_glr_auto_examples_linear_model_plot_lasso_and_elasticnet.py`
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compares Lasso with other L1-based regression models (ElasticNet and ARD
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Regression) for sparse signal recovery in the presence of noise and feature
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correlation.
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"""
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