Parameters for DBSCAN clustering (deprecated, use algorithm_params)
| Name | Type | Description | Notes |
|---|---|---|---|
| eps | float | Maximum distance between two samples for one to be considered in the neighborhood of the other | [optional] [default to 0.5] |
| min_samples | int | Number of samples in a neighborhood for a point to be considered a core point | [optional] [default to 5] |
| metric | str | Metric to use for distance computation | [optional] [default to 'euclidean'] |
| metric_params | Dict[str, object] | Additional keyword arguments for the metric function | [optional] |
| algorithm | str | Algorithm to compute pointwise distances and find nearest neighbors ('auto', 'ball_tree', 'kd_tree', 'brute') | [optional] [default to 'auto'] |
| leaf_size | int | Leaf size passed to BallTree or KDTree | [optional] [default to 30] |
| p | float | The power of the Minkowski metric to be used to calculate distance between points | [optional] [default to 2] |
| n_jobs | int | The number of parallel jobs to run (-1 means using all processors) | [optional] [default to 1] |
from mixpeek.models.dbscan_parameters import DbscanParameters
# TODO update the JSON string below
json = "{}"
# create an instance of DbscanParameters from a JSON string
dbscan_parameters_instance = DbscanParameters.from_json(json)
# print the JSON string representation of the object
print(DbscanParameters.to_json())
# convert the object into a dict
dbscan_parameters_dict = dbscan_parameters_instance.to_dict()
# create an instance of DbscanParameters from a dict
dbscan_parameters_from_dict = DbscanParameters.from_dict(dbscan_parameters_dict)