snowflake.ml.modeling.naive_bayes.CategoricalNB¶
- class snowflake.ml.modeling.naive_bayes.CategoricalNB(*, alpha=1.0, force_alpha='warn', fit_prior=True, class_prior=None, min_categories=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:
BaseTransformerNaive Bayes classifier for categorical features For more details on this class, see sklearn.naive_bayes.CategoricalNB
- alpha: float, default=1.0
Additive (Laplace/Lidstone) smoothing parameter (set alpha=0 and force_alpha=True, for no smoothing).
- force_alpha: bool, default=False
If False and alpha is less than 1e-10, it will set alpha to 1e-10. If True, alpha will remain unchanged. This may cause numerical errors if alpha is too close to 0.
- fit_prior: bool, default=True
Whether to learn class prior probabilities or not. If false, a uniform prior will be used.
- class_prior: array-like of shape (n_classes,), default=None
Prior probabilities of the classes. If specified, the priors are not adjusted according to the data.
- min_categories: int or array-like of shape (n_features,), default=None
Minimum number of categories per feature.
integer: Sets the minimum number of categories per feature to n_categories for each features.
array-like: shape (n_features,) where n_categories[i] holds the minimum number of categories for the ith column of the input.
None (default): Determines the number of categories automatically from the training data.
- 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 Naive Bayes classifier according to X, y For more details on this function, see sklearn.naive_bayes.CategoricalNB.fit
predict(dataset)Perform classification on an array of test vectors X For more details on this function, see sklearn.naive_bayes.CategoricalNB.predict
predict_log_proba(dataset[, output_cols_prefix])Return probability estimates for the test vector X For more details on this function, see sklearn.naive_bayes.CategoricalNB.predict_proba
predict_proba(dataset[, output_cols_prefix])Return probability estimates for the test vector X For more details on this function, see sklearn.naive_bayes.CategoricalNB.predict_proba
score(dataset)Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.naive_bayes.CategoricalNB.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.naive_bayes.CategoricalNB object.
Attributes
model_signaturesReturns model signature of current class.