snowflake.ml.modeling.neighbors.NearestCentroid¶

class snowflake.ml.modeling.neighbors.NearestCentroid(*, metric='euclidean', shrink_threshold=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

Nearest centroid classifier For more details on this class, see sklearn.neighbors.NearestCentroid

metric: str or callable, default=”euclidean”

Metric to use for distance computation. See the documentation of scipy.spatial.distance and the metrics listed in distance_metrics for valid metric values. Note that “wminkowski”, “seuclidean” and “mahalanobis” are not supported.

The centroids for the samples corresponding to each class is the point from which the sum of the distances (according to the metric) of all samples that belong to that particular class are minimized. If the “manhattan” metric is provided, this centroid is the median and for all other metrics, the centroid is now set to be the mean.

shrink_threshold: float, default=None

Threshold for shrinking centroids to remove features.

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 NearestCentroid model according to the given training data For more details on this function, see sklearn.neighbors.NearestCentroid.fit
predict(dataset)	Perform classification on an array of test vectors X For more details on this function, see sklearn.neighbors.NearestCentroid.predict
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.neighbors.NearestCentroid.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.neighbors.NearestCentroid object.

Attributes

model_signatures

Returns model signature of current class.