scikit-learn逻辑回归类库总结

类库种类

1. LogisticRegression

2.LogisticRegressionCV：使用了交叉验证来选择正则化系数C

 

重要参数

1. penalty：正则化方法

'l1' or 'l2', default: 'l2'

2. solver：优化器选择

solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag'}, default: 'liblinear'

3. C：正则化系数

C : float, default: 1.0

4. multi_class：多分类方法，

{'ovr', 'multinomial'}, default: 'ovr'

对于二分类来说这个参数没有意义。

OvR的思想很简单，无论你是多少元逻辑回归，我们都可以看做二元逻辑回归。具体做法是，对于第K类的分类决策，我们把所有第K类的样本作为正例，除了第K类样本以外的所有样本都作为负例，然后在上面做二元逻辑回归，得到第K类的分类模型。其他类的分类模型获得以此类推。

而MvM则相对复杂，这里举MvM的特例one-vs-one(OvO)作讲解。如果模型有T类，我们每次在所有的T类样本里面选择两类样本出来，不妨记为T1类和T2类，把所有的输出为T1和T2的样本放在一起，把T1作为正例，T2作为负例，进行二元逻辑回归，得到模型参数。我们一共需要T(T-1)/2次分类。

从上面的描述可以看出OvR相对简单，但分类效果相对略差（这里指大多数样本分布情况，某些样本分布下OvR可能更好）。而MvM分类相对精确，但是分类速度没有OvR快。

5. multi_class : str, {'ovr', 'multinomial'}, default: 'ovr'

class_weight参数用于标示分类模型中各种类型的权重，可以不输入，即不考虑权重，或者说所有类型的权重一样。如果选择输入的话，可以选择balanced让类库自己计算类型权重，或者我们自己输入各个类型的权重，比如对于0,1的二元模型，我们可以定义class_weight={0:0.9, 1:0.1}，这样类型0的权重为90%，而类型1的权重为10%。

如果class_weight选择balanced，那么类库会根据训练样本量来计算权重。某种类型样本量越多，则权重越低，样本量越少，则权重越高。

 

LogisticRegression全部参数

Parameters
----------
penalty : str, 'l1' or 'l2', default: 'l2'
    Used to specify the norm used in the penalization. The 'newton-cg',
    'sag' and 'lbfgs' solvers support only l2 penalties.

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.

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'* instead of deprecated
       *class_weight='auto'*.

max_iter : int, default: 100
    Useful only for the newton-cg, sag and lbfgs solvers.
    Maximum number of iterations taken for the solvers to converge.

random_state : int seed, RandomState instance, default: None
    The seed of the pseudo random number generator to use when
    shuffling the data. Used only in solvers 'sag' and 'liblinear'.

solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag'}, default: 'liblinear'
    Algorithm to use in the optimization problem.

    - For small datasets, 'liblinear' is a good choice, whereas 'sag' is
        faster for large ones.
    - For multiclass problems, only 'newton-cg', 'sag' and 'lbfgs' handle
        multinomial loss; 'liblinear' is limited to one-versus-rest
        schemes.
    - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty.

    Note that 'sag' fast convergence is only guaranteed on features with
    approximately the same scale. You can preprocess the data with a
    scaler from sklearn.preprocessing.

    .. versionadded:: 0.17
       Stochastic Average Gradient descent solver.

tol : float, default: 1e-4
    Tolerance for stopping criteria.

multi_class : str, {'ovr', 'multinomial'}, default: 'ovr'
    Multiclass option can be either 'ovr' or 'multinomial'. If the option
    chosen is 'ovr', then a binary problem is fit for each label. Else
    the loss minimised is the multinomial loss fit across
    the entire probability distribution. Works only for the 'newton-cg',
    'sag' and 'lbfgs' solver.

    .. versionadded:: 0.18
       Stochastic Average Gradient descent solver for 'multinomial' case.

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.

    .. versionadded:: 0.17
       *warm_start* to support *lbfgs*, *newton-cg*, *sag* solvers.

n_jobs : int, default: 1
    Number of CPU cores used during the cross-validation loop. If given
    a value of -1, all cores are used.