snowflake.ml.modeling.linear_model.LinearRegression¶
- class snowflake.ml.modeling.linear_model.LinearRegression(*, fit_intercept=True, copy_X=True, n_jobs=None, positive=False, 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:
BaseTransformerOrdinary least squares Linear Regression For more details on this class, see sklearn.linear_model.LinearRegression
- 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).
- copy_X: bool, default=True
If True, X will be copied; else, it may be overwritten.
- n_jobs: int, default=None
The number of jobs to use for the computation. This will only provide speedup in case of sufficiently large problems, that is if firstly n_targets > 1 and secondly X is sparse or if positive is set to True.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors. See Glossary for more details.- positive: bool, default=False
When set to
True, forces the coefficients to be positive. This option is only supported for dense arrays.- 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 linear model For more details on this function, see sklearn.linear_model.LinearRegression.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.LinearRegression.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.LinearRegression.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.LinearRegression object.
Attributes
model_signaturesReturns model signature of current class.