snowflake.ml.modeling.kernel_ridge.KernelRidge¶

class snowflake.ml.modeling.kernel_ridge.KernelRidge(*, alpha=1, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=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

Kernel ridge regression For more details on this class, see sklearn.kernel_ridge.KernelRidge

alpha: float or array-like of shape (n_targets,), default=1.0

Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. See ridge_regression for formula.

kernel: str or callable, default=”linear”

Kernel mapping used internally. This parameter is directly passed to pairwise_kernel. If kernel is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS or “precomputed”. If kernel is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if kernel is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from sklearn.metrics.pairwise are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.

gamma: float, default=None

Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.

degree: int, default=3

Degree of the polynomial kernel. Ignored by other kernels.

coef0: float, default=1

Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

kernel_params: dict, default=None

Additional parameters (keyword arguments) for kernel function passed as callable object.

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 Kernel Ridge regression model For more details on this function, see sklearn.kernel_ridge.KernelRidge.fit
predict(dataset)	Predict using the kernel ridge model For more details on this function, see sklearn.kernel_ridge.KernelRidge.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.kernel_ridge.KernelRidge.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.kernel_ridge.KernelRidge object.

Attributes

model_signatures

Returns model signature of current class.