verticapy.machine_learning.vertica.cluster.DBSCAN.fit#
- DBSCAN.fit(input_relation: str | vDataFrame, X: str | list[str] | None = None, key_columns: str | list[str] | None = None, index: str | None = None, return_report: bool = False) None #
Trains the model.
Parameters#
- input_relation: SQLRelation
Training relation.
- X: SQLColumns, optional
list
of the predictors. If empty, all the numerical :py:class:`~vDataColumn are used.- key_columns: SQLColumns, optional
Columns not used during the algorithm computation but which are used to create the final relation.
- index: str, optional
Index used to identify each row separately. It is highly recommanded to have one already in the main table to avoid creating temporary tables.
Examples#
Let’s start by importing
verticapy
:import verticapy as vp
For this example, we will create a small dataset.
data = vp.vDataFrame({"col":[1.2, 1.1, 1.3, 1.5, 2, 2.2, 1.09, 0.9, 100, 102]})
Then we import the model:
from verticapy.machine_learning.vertica import DBSCAN
Then we can create the model:
model = DBSCAN( eps = 0.5, min_samples = 2, p = 2, )
Once the model is initialized we can fit the model:
model.fit(data, X = ["col"])
Note
Refer to
DBSCAN
for more information about the different methods and usages.