snowflake.ml.modeling.linear_model.LarsCV¶
- class snowflake.ml.modeling.linear_model.LarsCV(*, fit_intercept=True, verbose=False, max_iter=500, normalize='deprecated', precompute='auto', cv=None, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True, 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:
BaseTransformerCross-validated Least Angle Regression model For more details on this class, see sklearn.linear_model.LarsCV
- fit_intercept: bool, default=True
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
- verbose: bool or int, default=False
Sets the verbosity amount.
- max_iter: int, default=500
Maximum number of iterations to perform.
- normalize: bool, default=False
This parameter is ignored when
fit_interceptis set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please useStandardScalerbefore callingfiton an estimator withnormalize=False.- precompute: bool, ‘auto’ or array-like , default=’auto’
Whether to use a precomputed Gram matrix to speed up calculations. If set to
'auto'let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X.- cv: int, cross-validation generator or an iterable, default=None
Determines the cross-validation splitting strategy. Possible inputs for cv are:
None, to use the default 5-fold cross-validation,
integer, to specify the number of folds.
CV splitter,
An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs,
KFoldis used.Refer User Guide for the various cross-validation strategies that can be used here.
- max_n_alphas: int, default=1000
The maximum number of points on the path used to compute the residuals in the cross-validation.
- n_jobs: int or None, default=None
Number of CPUs to use during the cross validation.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors. See Glossary for more details.- eps: float, default=np.finfo(float).eps
The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the
tolparameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization.- copy_X: bool, default=True
If
True, X will be copied; else, it may be overwritten.- 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
fit(dataset)Fit the model using X, y as training data For more details on this function, see sklearn.linear_model.LarsCV.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.LarsCV.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.LarsCV.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.LarsCV object.
Attributes
model_signaturesReturns model signature of current class.