elbow¶

In [ ]:

elbow(input_relation: (str, vDataFrame),
      X: list = [],
      n_cluster: (tuple, list) = (1, 15),
      init: (str, list) = "kmeanspp",
      max_iter: int = 50,
      tol: float = 1e-4,
      ax=None,
      **style_kwds,)

Draws an Elbow curve.

Parameters¶

Name	Type	Optional	Description
input_relation	str	❌	Relation to use to train the model.
X	list	✓	List of the predictor columns. If empty all the numerical vcolumns will be used.
n_cluster	tuple / list	✓	Tuple representing the number of cluster to start with and to end with. It can also be customized list with the different K to test.
init	str / list	✓	The method to use to find the initial cluster centers. kmeanspp : Uses the KMeans++ method to initialize the centers. random : The initial centers. It can be also a list with the initial cluster centers to use.
max_iter	int	✓	The maximum number of iterations the algorithm performs.
tol	float	✓	Determines whether the algorithm has converged. The algorithm is considered converged after no center has moved more than a distance of 'tol' from the previous iteration.
ax	Matplotlib axes object	✓	The axes to plot on.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [8]:

from verticapy.learn.model_selection import elbow
elbow(input_relation = "public.iris",
      X = ["PetalLengthCm", "PetalWidthCm"])

Out[8]:

	Within-Cluster SS
1	0.0
2	0.8430854905080415
3	0.9429226137921877
4	0.9631049651782849
5	0.974234707623065
6	0.9757984417757647
7	0.981234463744753
8	0.9849184082760484
9	0.9832711354953506
10	0.986486392002433
11	0.9898109388276228
12	0.9900775360143651
13	0.9914697617316701
14	0.9924304741142214

Rows: 1-14 | Columns: 2