Parameters for HDBSCAN clustering (deprecated, use algorithm_params)
| Name | Type | Description | Notes |
|---|---|---|---|
| min_cluster_size | int | Minimum number of samples in a cluster | [optional] [default to 5] |
| min_samples | int | Number of samples in a neighborhood for a point to be considered a core point. Defaults to min_cluster_size if None | [optional] |
| cluster_selection_epsilon | float | A distance threshold for cluster selection. Clusters below this value will be merged | [optional] [default to 0] |
| max_cluster_size | int | Maximum number of samples in a cluster. Clusters above this size will be split | [optional] |
| metric | str | Metric to use for distance computation | [optional] [default to 'euclidean'] |
| alpha | float | A distance scaling parameter | [optional] [default to 1] |
| cluster_selection_method | str | Method to select clusters from the condensed tree ('eom' or 'leaf') | [optional] [default to 'eom'] |
| allow_single_cluster | bool | Allow HDBSCAN to find only a single cluster | [optional] [default to False] |
| prediction_data | bool | Whether to generate extra data for predicting cluster membership | [optional] [default to False] |
| match_reference_implementation | bool | Whether to match the reference implementation exactly | [optional] [default to False] |
from mixpeek.models.hdbscan_parameters import HdbscanParameters
# TODO update the JSON string below
json = "{}"
# create an instance of HdbscanParameters from a JSON string
hdbscan_parameters_instance = HdbscanParameters.from_json(json)
# print the JSON string representation of the object
print(HdbscanParameters.to_json())
# convert the object into a dict
hdbscan_parameters_dict = hdbscan_parameters_instance.to_dict()
# create an instance of HdbscanParameters from a dict
hdbscan_parameters_from_dict = HdbscanParameters.from_dict(hdbscan_parameters_dict)