verticapy.machine_learning.vertica.cluster.DBSCAN.plot#
- DBSCAN.plot(max_nb_points: int = 100, chart: PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure | None = None, **style_kwargs) PlottingBase | TableSample | Axes | mFigure | Highchart | Highstock | Figure #
Draws the model.
Parameters#
- max_nb_points: int
Maximum number of points to display.
- chart: PlottingObject, optional
The chart object to plot on.
- **style_kwargs
Any optional parameter to pass to the Plotting functions.
Returns#
- obj
Plotting Object.
Examples#
Let’s start by importing
verticapy
:import verticapy as vp
For this example, we will create a small dataset.
data = vp.vDataFrame( { "col1": [1.2, 1.1, 1.3, 1.5, 2, 2.2, 1.09, 0.9, 100, 102], "col2": [2.2, 2.1, 4.3, 5.5, 6, 2, 9, 1, 110, 120], }, )
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 = ["col1", "col2"])
And lastly we can plot the model:
model.plot()
Note
Refer to
DBSCAN
for more information about the different methods and usages.