snowflake.ml.modeling.discriminant_analysis.QuadraticDiscriminantAnalysis¶

class snowflake.ml.modeling.discriminant_analysis.QuadraticDiscriminantAnalysis(*, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001, 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

Quadratic Discriminant Analysis For more details on this class, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis

priors: array-like of shape (n_classes,), default=None

Class priors. By default, the class proportions are inferred from the training data.

reg_param: float, default=0.0

Regularizes the per-class covariance estimates by transforming S2 as S2 = (1 - reg_param) * S2 + reg_param * np.eye(n_features), where S2 corresponds to the scaling_ attribute of a given class.

store_covariance: bool, default=False

If True, the class covariance matrices are explicitly computed and stored in the self.covariance_ attribute.

tol: float, default=1.0e-4

Absolute threshold for a singular value to be considered significant, used to estimate the rank of Xk where Xk is the centered matrix of samples in class k. This parameter does not affect the predictions. It only controls a warning that is raised when features are considered to be colinear.

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])	Apply decision function to an array of samples For more details on this function, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function
fit(dataset)	Fit the model according to the given training data and parameters For more details on this function, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.fit
predict(dataset)	Perform classification on an array of test vectors X For more details on this function, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict
predict_log_proba(dataset[, output_cols_prefix])	Return posterior probabilities of classification For more details on this function, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba
predict_proba(dataset[, output_cols_prefix])	Return posterior probabilities of classification For more details on this function, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis object.

Attributes

model_signatures

Returns model signature of current class.