Logistic Regression¶
Logistic regression of a categorical dependent variable
Configuration: 


Attributes: 

Inputs:  
Outputs: 

Ports:
Outputs:
model: model
Model
Configuration:
 penalty
Used to specify the norm used in the penalization. The ‘newtoncg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties.
New in version 0.19: l1 penalty with SAGA solver (allowing ‘multinomial’ + L1)
 dual
 Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.
 C
 Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.
 fit_intercept
 Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
 intercept_scaling
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
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.
New in version 0.17: class_weight=’balanced’
 tol
 Tolerance for stopping criteria.
 multi_class
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. Does not work for liblinear solver.
New in version 0.18: Stochastic Average Gradient descent solver for ‘multinomial’ case.
 max_iter
 Useful only for the newtoncg, sag and lbfgs solvers. Maximum number of iterations taken for the solvers to converge.
 solver
default: ‘liblinear’ 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 ‘newtoncg’, ‘sag’, ‘saga’ and ‘lbfgs’
 handle multinomial loss; ‘liblinear’ is limited to oneversusrest schemes.
 ‘newtoncg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty, whereas
 ‘liblinear’ and ‘saga’ handle L1 penalty.
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.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
 n_jobs
 Number of CPU cores used when parallelizing over classes if multi_class=”ovr”. Ignored when the solver is set to “liblinear” regardless of multi_class. If given 1 then all cores are used
 random_state
 The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when
solver
== ‘sag’ or ‘liblinear’. warm_start
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.
New in version 0.17: warm_start to support lbfgs, newtoncg, sag, saga solvers.
Some of the docstrings for this module have been automatically extracted from the scikitlearn library and are covered by their respective licenses.