Model.fit¶

In [ ]:

Model.fit(input_relation: (str, vDataFrame), 
          X: list = [])

Trains the model.

Parameters¶

Name	Type	Optional	Description
input_relation	str	❌	Training relation.
X	list	✓	List of the predictors. If empty, all the numerical vcolumns will be used.

Returns¶

object : self

Example¶

In [70]:

from verticapy.learn.cluster import KMeans
model = KMeans(name = "public.KMeans_iris",
               n_cluster = 3)
model.fit("public.iris", 
          ["PetalLengthCm", "PetalWidthCm"])

Out[70]:


=======
centers
=======
petallengthcm|petalwidthcm
-------------+------------
   1.46400   |   0.24400  
   5.62609   |   2.04783  
   4.29259   |   1.35926  


=======
metrics
=======
Evaluation metrics:
     Total Sum of Squares: 550.64347
     Within-Cluster Sum of Squares: 
         Cluster 0: 2.0384
         Cluster 1: 15.163478
         Cluster 2: 14.227407
     Total Within-Cluster Sum of Squares: 31.429286
     Between-Cluster Sum of Squares: 519.21418
     Between-Cluster SS / Total SS: 94.29%
 Number of iterations performed: 7
 Converged: True
 Call:
kmeans('public.KMeans_iris', 'public.iris', '"PetalLengthCm", "PetalWidthCm"', 3
USING PARAMETERS max_iterations=300, epsilon=0.0001, init_method='kmeanspp', distance_method='euclidean')