8000 Fix doc Multitasks · scikit-learn/scikit-learn@6416b80 · GitHub
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Fix doc Multitasks
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sklearn/linear_model/coordinate_descent.py

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@@ -1611,7 +1611,8 @@ class MultiTaskElasticNet(Lasso):
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coef_ : array, shape (n_tasks, n_features)
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Parameter vector (W in the cost function formula). If a 1D y is \
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passed in at fit (non multi-task usage), ``coef_`` is then a 1D array
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passed in at fit (non multi-task usage), ``coef_`` is then a 1D array.
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Note that ``coef_`` stores the transpose of ``W``, ``W.T``.
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n_iter_ : int
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number of iterations run by the coordinate descent solver to reach
@@ -1792,7 +1793,8 @@ class MultiTaskLasso(MultiTaskElasticNet):
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Attributes
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----------
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coef_ : array, shape (n_tasks, n_features)
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parameter vector (W in the cost function formula)
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Parameter vector (W in the cost function formula).
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Note that ``coef_`` stores the transpose of ``W``, ``W.T``.
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intercept_ : array, shape (n_tasks,)
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independent term in decision function.
@@ -1950,6 +1952,7 @@ class MultiTaskElasticNetCV(LinearModelCV, RegressorMixin):
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coef_ : array, shape (n_tasks, n_features)
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Parameter vector (W in the cost function formula).
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Note that ``coef_`` stores the transpose of ``W``, ``W.T``.
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alpha_ : float
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The amount of penalization chosen by cross validation
@@ -2115,6 +2118,7 @@ class MultiTaskLassoCV(LinearModelCV, RegressorMixin):
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coef_ : array, shape (n_tasks, n_features)
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Parameter vector (W in the cost function formula).
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Note that ``coef_`` stores the transpose of ``W``, ``W.T``.
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alpha_ : float
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The amount of penalization chosen by cross validation

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