snowflake.ml.modeling.linear_model.LogisticRegressionCV¶

class snowflake.ml.modeling.linear_model.LogisticRegressionCV(*, Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None, solver='lbfgs', tol=0.0001, max_iter=100, class_weight=None, n_jobs=None, verbose=0, refit=True, intercept_scaling=1.0, multi_class='auto', random_state=None, l1_ratios=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Logistic Regression CV (aka logit, MaxEnt) classifier For more details on this class, see sklearn.linear_model.LogisticRegressionCV

Cs: int or list of floats, default=10

Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e-4 and 1e4. 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.

cv: int or cross-validation generator, default=None

The default cross-validation generator used is Stratified K-Folds. If an integer is provided, then it is the number of folds used. See the module sklearn.model_selection module for the list of possible cross-validation objects.

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.

penalty: {‘l1’, ‘l2’, ‘elasticnet’}, default=’l2’

Specify the norm of the penalty:

‘l2’: add a L2 penalty term (used by default);
‘l1’: add a L1 penalty term;
‘elasticnet’: both L1 and L2 penalty terms are added.

scoring: str or callable, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). For a list of scoring functions that can be used, look at sklearn.metrics. The default scoring option used is ‘accuracy’.

solver: {‘lbfgs’, ‘liblinear’, ‘newton-cg’, ‘newton-cholesky’, ‘sag’, ‘saga’}, default=’lbfgs’

Algorithm to use in the optimization problem. Default is ‘lbfgs’. To choose a solver, you might want to consider the following aspects:

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’ might be slower in LogisticRegressionCV because it does not handle warm-starting. ‘liblinear’ is limited to one-versus-rest schemes.

‘newton-cholesky’ is a good choice for n_samples >> n_features, especially with one-hot encoded categorical features with rare categories. Note that it is limited to binary classification and the one-versus-rest reduction for multiclass classification. Be aware that the memory usage of this solver has a quadratic dependency on n_features because it explicitly computes the Hessian matrix.

‘lbfgs’ - [‘l2’]

‘liblinear’ - [‘l1’, ‘l2’]

‘newton-cg’ - [‘l2’]

‘newton-cholesky’ - [‘l2’]

‘sag’ - [‘l2’]

‘saga’ - [‘elasticnet’, ‘l1’, ‘l2’]

tol: float, default=1e-4

Tolerance for stopping criteria.

max_iter: int, default=100

Maximum number of iterations of the optimization algorithm.

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.

n_jobs: int, default=None

Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

verbose: int, default=0

For the ‘liblinear’, ‘sag’ and ‘lbfgs’ solvers set verbose to any positive number for verbosity.

refit: bool, default=True

If set to True, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged.

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.

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’.

random_state: int, RandomState instance, default=None

Used when solver=’sag’, ‘saga’ or ‘liblinear’ to shuffle the data. Note that this only applies to the solver and not the cross-validation generator. See Glossary for details.

l1_ratios: list of float, default=None

The list of Elastic-Net mixing parameter, with 0 <= l1_ratio <= 1. Only used if penalty='elasticnet'. A value of 0 is equivalent to using penalty='l2', while 1 is equivalent to using penalty='l1'. For 0 < l1_ratio <1, the penalty is a combination of L1 and L2.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

`decision_function`(dataset[, output_cols_prefix])	Predict confidence scores for samples For more details on this function, see sklearn.linear_model.LogisticRegressionCV.decision_function
`fit`(dataset)	Fit the model according to the given training data For more details on this function, see sklearn.linear_model.LogisticRegressionCV.fit
`predict`(dataset)	Predict class labels for samples in X For more details on this function, see sklearn.linear_model.LogisticRegressionCV.predict
`predict_log_proba`(dataset[, output_cols_prefix])	Probability estimates For more details on this function, see sklearn.linear_model.LogisticRegressionCV.predict_proba
`predict_proba`(dataset[, output_cols_prefix])	Probability estimates For more details on this function, see sklearn.linear_model.LogisticRegressionCV.predict_proba
`score`(dataset)	Score using the scoring option on the given test data and labels For more details on this function, see sklearn.linear_model.LogisticRegressionCV.score
`set_input_cols`(input_cols)	Input columns setter.
`to_sklearn`()	Get sklearn.linear_model.LogisticRegressionCV object.

Attributes

model_signatures

Returns model signature of current class.