sklearn.linear_mode中的LogisticRegression函数的简介、使用方法
class LogisticRegression Found at: sklearn.linear_model._logisticclass LogisticRegression(BaseEstimator, LinearClassifierMixin, SparseCoefMixin):
"""
Logistic Regression (aka logit, MaxEnt) classifier.
In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the cross-entropy loss if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs', 'sag', 'saga' and 'newton-cg' solvers.)
This class implements regularized logistic regression using the 'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note that regularization is applied by default**. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied).
The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization with primal formulation, or no regularization. The 'liblinear' solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only supported by the 'saga' solver.
Read more in the :ref:`User Guide <logistic_regression>`.
逻辑回归(又名logit, MaxEnt)分类器。
在多类情况下，如果“multi_class”选项设置为“OvR”，训练算法使用one vs-rest (OvR)方案，如果“multi_class”选项设置为“多项”，训练算法使用交叉熵损失。(目前，“多项”选项仅由“lbfgs”、“sag”、“saga”和“newton-cg”求解器支持。)
这个类使用“liblinear”库、“newton-cg”、“sag”、“saga”和“lbfgs”求解器实现正则逻辑回归。**注意正则化是在默认情况下应用的**。它可以处理稠密和稀疏输入。使用C-ordered数组或包含64位浮点数的CSR矩阵，以获得最佳性能;任何其他输入格式都将被转换(和复制)。
“newton-cg”、“sag”和“lbfgs”求解器只支持使用原始公式的L2正则化，或者不支持正则化。“liblinear”求解器支持L1和L2正则化，只有L2惩罚的对偶公式。弹性网正则化仅由“saga”求解器支持。
详见:ref: ' User Guide <logistic_regression> '。</logistic_regression>
Parameters
----------
penalty : {'l1', 'l2', 'elasticnet', 'none'}, default='l2'
Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is only supported by the 'saga' solver. If 'none' (not supported by the liblinear solver), no regularization is applied.
.. versionadded:: 0.19
l1 penalty with SAGA solver (allowing 'multinomial' + L1)
dual : bool, default=False
Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.
tol : float, default=1e-4
Tolerance for stopping criteria.
C : float, default=1.0
Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.
fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
intercept_scaling : float, default=1
Useful only when the solver 'liblinear' is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a "synthetic" feature with constant value equal to intercept_scaling is appended to the instance vector.The intercept becomes ``intercept_scaling * synthetic_feature_weight``.
Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
class_weight : dict or 'balanced', default=None
Weights associated with classes in the form ``{class_label: weight}``. If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
.. versionadded:: 0.17
*class_weight='balanced'*
random_state : int, RandomState instance, default=None Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the data. See :term:`Glossary <random_state>` for details.
solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'}, \ default='lbfgs'
Algorithm to use in the optimization problem.
- For small datasets, 'liblinear' is a good choice, whereas 'sag' and 'saga' are faster for large ones.
- For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs' handle multinomial loss; 'liblinear' is limited to one-versus-rest schemes.
- 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty
- 'liblinear' and 'saga' also handle L1 penalty
- 'saga' also supports 'elasticnet' penalty
- 'liblinear' does not support setting ``penalty='none'``
Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. 参数
---------
处罚:{l1, l2,‘elasticnet’,‘没有’},默认=“l2”
用于指定在处罚中使用的规范。“newton-cg”，“sag”和“lbfgs”求解器只支持l2惩罚。“elasticnet”仅由“saga”求解器支持。如果“none”(liblinear求解器不支持)，则不应用正则化。
. .versionadded:: 0.19
l1惩罚与SAGA求解器(允许“多项”+ l1)
bool，默认=False
双重或原始配方。对偶公式仅适用于l2罚用线性求解器。当n_samples > n_features时，preferred dual=False。
tol:浮动，默认=1e-4
停止标准的容忍度。
C: float, default=1.0
正则化强度的逆;必须是正浮点数。与支持向量机一样，值越小，正则化越强。
fit_intercept: bool，默认=True
指定一个常数(即偏差或拦截)是否应该添加到决策函数中。
intercept_scaling:浮动，默认=1
只有在使用“liblinear”求解器和self时才有用。fit_intercept设置为True。在这种情况下，x变成[x, self。intercept_scaling]，即。一个常数值等于intercept_scaling的“合成”特性被附加到实例向量中。拦截变成' ' intercept_scaling * synthetic_feature_weight ' '。
注意!合成特征权重与所有其他特征一样，采用l1/l2正则化。为了减少正则化对合成特征权重的影响(因此对拦截的影响)，必须增加intercept_scaling。
class_weight: dict或'balanced'，默认为None
以' ' {class_label: weight} ' ' '形式关联类的权重。如果没有给出，所有类的权重都应该是1。
“平衡”模式使用y的值自动调整权重与输入数据中的类频率成反比，如' ' n_samples / (n_classes * np.bincount(y)) ' '。
注意，如果指定了sample_weight，那么这些权重将与sample_weight相乘(通过fit方法传递)。
. .versionadded:: 0.17
* class_weight = '平衡' *
random_state: int, RandomState instance, default=None，当' ' solver ' ' = 'sag'， 'saga'或'liblinear'洗发数据时使用。详见:term: ' Glossary <random_state> '。</random_state>
解决:{‘newton-cg’,‘lbfgs’,‘liblinear’,“凹陷”,“传奇”},\默认=“lbfgs”
算法用于优化问题。
对于小数据集，“liblinear”是一个不错的选择，而“sag”和“saga”对于大数据集更快。
-对于多类问题，只有“newton-cg”、“sag”、“saga”和“lbfgs”处理多项损失;“liblinear”仅限于“一对二”方案。
- 'newton-cg'， 'lbfgs'， 'sag'和'saga'处理L2或没有处罚
-“liblinear”和“saga”也可以处理L1惩罚
-《英雄传奇》也支持《弹性网》的惩罚
- 'liblinear'不支持设置' ' penalty='none' ' '
请注意，“sag”和“saga”的快速收敛只能保证在大致相同规模的特性上。您可以使用sklearn.preprocessing中的scaler对数据进行预处理。
.. versionadded:: 0.17
Stochastic Average Gradient descent solver.
.. versionadded:: 0.19
SAGA solver.
.. versionchanged:: 0.22
The default solver changed from 'liblinear' to 'lbfgs' in 0.22.
max_iter : int, default=100
Maximum number of iterations taken for the solvers to converge.
multi_class : {'auto', 'ovr', 'multinomial'}, default='auto'
If the option chosen is 'ovr', then a binary problem is fit for each label. For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, *even when the data is binary*. 'multinomial' is unavailable when solver='liblinear'. 'auto' selects 'ovr' if the data is binary, or if solver='liblinear', and otherwise selects 'multinomial'.
.. versionadded:: 0.18
Stochastic Average Gradient descent solver for 'multinomial' case.
.. versionchanged:: 0.22
Default changed from 'ovr' to 'auto' in 0.22.
verbose : int, default=0
For the liblinear and lbfgs solvers set verbose to any positive number for verbosity.
warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver. See :term:`the Glossary <warm_start>`.
.. versionadded:: 0.17
*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.
n_jobs : int, default=None
Number of CPU cores used when parallelizing over classes if multi_class='ovr'". This parameter is ignored when the ``solver`` is set to 'liblinear' regardless of whether 'multi_class' is specified or not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors.
See :term:`Glossary <n_jobs>` for more details.
l1_ratio : float, default=None
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a combination of L1 and L2.
. .versionadded:: 0.17
随机平均梯度下降求解器。
. .versionadded:: 0.19
SAGA solver。
. .versionchanged:: 0.22
在0.22中，默认求解器从“liblinear”更改为“lbfgs”。
max_iter: int，默认=100
使求解器收敛的最大迭代次数。
multi_class: {'auto'， 'ovr'， '多项'}，默认='auto'
如果选择的选项是'ovr'，那么每个标签都适合一个二进制问题。对于“多项”损失最小化是多项式损失适合整个概率分布，即使当数据是二进制*。当求解器='liblinear'时，不可用多项式。auto选择'ovr'如果数据是二进制的，或者solver='liblinear'，否则选择'多项'。
. .versionadded:: 0.18
“多项式”情况的随机平均梯度下降求解器。
. .versionchanged:: 0.22
在0.22中默认从“ovr”改为“auto”。
int，默认=0
对于liblinear和lbfgs求解器，将冗长设置为任意正数。
warm_start: bool，默认=False
当设置为True时，重用前面调用的解决方案以适合初始化，否则就擦除前面的解决方案。对于线性求解器是没用的。参见:term: ' the Glossary <warm_start> '。</warm_start>
. .versionadded:: 0.17
*warm_start*支持*lbfgs*， *newton-cg*， *sag*， *saga*求解器。
n_jobs: int，默认=无
如果multi_class='ovr'"，则在类上并行时使用的CPU核数。当' ' solver ' '被设置为'liblinear'时，不管'multi_class'是否被指定，这个参数都会被忽略。' ' None ' '表示1，除非在:obj: ' joblib.parallel_backend '上下文中。“-1”表示使用所有处理器。
有关更多细节，请参见:term: ' Glossary <n_jobs> '。</n_jobs>
l1_ratio: float, default=None
弹网混合参数``0 <= l1_ratio <= 1``。只在``penalty= ` elasticnet ``时使用。设置' ' l1_ratio=0 ' '等价于使用' ' penalty='l2' ' '，设置' ' l1_ratio=1 ' '等价于使用' ' penalty='l1' ' '。对于' ' 0 < l1_ratio <1 ' '，惩罚是L1和L2的组合。
Attributes
----------
classes_ : ndarray of shape (n_classes, )
A list of class labels known to the classifier.
coef_ : ndarray of shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function.
`coef_` is of shape (1, n_features) when the given problem is binary.
In particular, when `multi_class='multinomial'`, `coef_` corresponds to outcome 1 (True) and `-coef_` corresponds to outcome 0 (False).
intercept_ : ndarray of shape (1,) or (n_classes,)
Intercept (a.k.a. bias) added to the decision function.
If `fit_intercept` is set to False, the intercept is set to zero.
`intercept_` is of shape (1,) when the given problem is binary. In particular, when `multi_class='multinomial'`, `intercept_` corresponds to outcome 1 (True) and `-intercept_` corresponds to outcome 0 (False).
n_iter_ : ndarray of shape (n_classes,) or (1, )
Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element. For liblinear solver, only the maximum number of iteration across all classes is given.
.. versionchanged:: 0.20
In SciPy <= 1.0.0 the number of lbfgs iterations may exceed ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
See Also
--------
SGDClassifier : Incrementally trained logistic regression (when given the parameter ``loss="log"``).
LogisticRegressionCV : Logistic regression with built-in cross validation.
Notes
-----
The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter.
Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation.
属性
----------
classes_:形状的ndarray
分类器已知的类标签列表。
coef_:决策函数中特征的形状(1,n_features)或(n_classes, n_features)系数的ndarray。
当给定的问题是二进制时，' coef_ '是形状(1,n_features)。
特别是，当“multi_class=”多项“”时，“coef_”对应结果1 (True)，而“-coef_”对应结果0 (False)。
intercept_:形状(1，)或(n_classes，)的ndarray
在决策函数中加入截距(即偏差)。
如果' fit_intercept '设置为False，则拦截设置为零。
当给定的问题是二进制时，intercept_ '的形状是(1，)。特别是，当“multi_class=”多项“”时，“intercept_”对应结果1 (True)，而“-intercept_”对应结果0 (False)。
n_iter_:形状(n_classes，)或(1，)的ndarray
所有类的实际迭代次数。如果是二项或多项，则只返回1个元素。对于线性求解器，只给出了所有类的最大迭代次数。
. .versionchanged:: 0.20
在SciPy <= 1.0.0中，lbfgs迭代次数可能超过' ' max_iter ' '。' ' n_iter_ ' '现在最多报告' ' max_iter ' '。
另请参阅
--------
增量训练逻辑回归(当给定参数' ' loss="log" ' ')。
逻辑回归cv:内置交叉验证的逻辑回归。
