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DOC added intervals for parameters for 4 glm classes #22076

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Merged
merged 6 commits into from
Jan 24, 2022
Merged

DOC added intervals for parameters for 4 glm classes #22076

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e19e0e7
added intervals for parameters for 4 glm classes
reshamas Dec 26, 2021
2bdba0e
Merge branch 'main' of github.com:scikit-learn/scikit-learn into glm_…
reshamas Jan 1, 2022
482db41
verbose min should be 0, not 1
reshamas Jan 2, 2022
2d602c1
Merge branch 'main' of github.com:scikit-learn/scikit-learn into glm_…
reshamas Jan 13, 2022
13cbc02
change 'should' to 'must'; verbose min val should be 0
reshamas Jan 13, 2022
1d25e02
verbose min should be 0 !!!!
reshamas Jan 13, 2022
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16 changes: 16 additions & 0 deletions sklearn/linear_model/_glm/glm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,6 +72,7 @@ class GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):
regularization strength. ``alpha = 0`` is equivalent to unpenalized
GLMs. In this case, the design matrix `X` must have full column rank
(no collinearities).
Values must be in the range `[0.0, inf)`.

fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be
Expand Down Expand Up @@ -99,19 +100,22 @@ class GeneralizedLinearRegressor(RegressorMixin, BaseEstimator):

max_iter : int, default=100
The maximal number of iterations for the solver.
Values must be in the range `[1, inf)`.

tol : float, default=1e-4
Stopping criterion. For the lbfgs solver,
the iteration will stop when ``max{|g_j|, j = 1, ..., d} <= tol``
where ``g_j`` is the j-th component of the gradient (derivative) of
the objective function.
Values must be in the range `(0.0, inf)`.

warm_start : bool, default=False
If set to ``True``, reuse the solution of the previous call to ``fit``
as initialization for ``coef_`` and ``intercept_``.

verbose : int, default=0
For the lbfgs solver set verbose to any positive number for verbosity.
Values must be in the range `[0, inf)`.

Attributes
----------
Expand Down Expand Up @@ -445,26 +449,30 @@ class PoissonRegressor(GeneralizedLinearRegressor):
regularization strength. ``alpha = 0`` is equivalent to unpenalized
GLMs. In this case, the design matrix `X` must have full column rank
(no collinearities).
Values must be in the range `[0.0, inf)`.

fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the linear predictor (X @ coef + intercept).

max_iter : int, default=100
The maximal number of iterations for the solver.
Values must be in the range `[1, inf)`.

tol : float, default=1e-4
Stopping criterion. For the lbfgs solver,
the iteration will stop when ``max{|g_j|, j = 1, ..., d} <= tol``
where ``g_j`` is the j-th component of the gradient (derivative) of
the objective function.
Values must be in the range `(0.0, inf)`.

warm_start : bool, default=False
If set to ``True``, reuse the solution of the previous call to ``fit``
as initialization for ``coef_`` and ``intercept_`` .

verbose : int, default=0
For the lbfgs solver set verbose to any positive number for verbosity.
Values must be in the range `[0, inf)`.

Attributes
----------
Expand Down Expand Up @@ -562,26 +570,30 @@ class GammaRegressor(GeneralizedLinearRegressor):
regularization strength. ``alpha = 0`` is equivalent to unpenalized
GLMs. In this case, the design matrix `X` must have full column rank
(no collinearities).
Values must be in the range `[0.0, inf)`.

fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the linear predictor (X @ coef + intercept).

max_iter : int, default=100
The maximal number of iterations for the solver.
Values must be in the range `[1, inf)`.

tol : float, default=1e-4
Stopping criterion. For the lbfgs solver,
the iteration will stop when ``max{|g_j|, j = 1, ..., d} <= tol``
where ``g_j`` is the j-th component of the gradient (derivative) of
the objective function.
Values must be in the range `(0.0, inf)`.

warm_start : bool, default=False
If set to ``True``, reuse the solution of the previous call to ``fit``
as initialization for ``coef_`` and ``intercept_`` .

verbose : int, default=0
For the lbfgs solver set verbose to any positive number for verbosity.
Values must be in the range `[0, inf)`.

Attributes
----------
Expand Down Expand Up @@ -700,6 +712,7 @@ class TweedieRegressor(GeneralizedLinearRegressor):
regularization strength. ``alpha = 0`` is equivalent to unpenalized
GLMs. In this case, the design matrix `X` must have full column rank
(no collinearities).
Values must be in the range `[0.0, inf)`.

fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be
Expand All @@ -715,19 +728,22 @@ class TweedieRegressor(GeneralizedLinearRegressor):

max_iter : int, default=100
The maximal number of iterations for the solver.
Values must be in the range `[1, inf)`.

tol : float, default=1e-4
Stopping criterion. For the lbfgs solver,
the iteration will stop when ``max{|g_j|, j = 1, ..., d} <= tol``
where ``g_j`` is the j-th component of the gradient (derivative) of
the objective function.
Values must be in the range `(0.0, inf)`.

warm_start : bool, default=False
If set to ``True``, reuse the solution of the previous call to ``fit``
as initialization for ``coef_`` and ``intercept_`` .

verbose : int, default=0
For the lbfgs solver set verbose to any positive number for verbosity.
Values must be in the range `[0, inf)`.

Attributes
----------
Expand Down