best_k

In [ ]:
best_k(input_relation: (str, vDataFrame),
       X: list = [],
       n_cluster: (tuple, list) = (1, 100),
       init: (str, list) = "kmeanspp",
       max_iter: int = 50,
       tol: float = 1e-4,
       elbow_score_stop: float = 0.8)

Finds the k-means k based on a score.

Parameters

Name Type Optional Description
input_relation
str / vDataFrame
❌
 Relation to use to train the model.
X
list
✓
 List of the predictor columns. If empty, all the numerical columns 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.
elbow_score_stop
float
✓
 Stops the Parameters Search when this Elbow score is reached.

Returns

int : the k-means k.

Example

In [3]:
from verticapy.learn.model_selection import best_k
best_k(input_relation = "public.iris",
       X = ["PetalLengthCm", "PetalWidthCm"],
       elbow_score_stop = 0.9)
Out[3]:
3