# KMeansSummary¶

class pyspark.ml.clustering.KMeansSummary(java_obj: Optional[JavaObject] = None)

Summary of KMeans.

Attributes

 cluster DataFrame of predicted cluster centers for each training data point. clusterSizes Size of (number of data points in) each cluster. featuresCol Name for column of features in predictions. k The number of clusters the model was trained with. numIter Number of iterations. predictionCol Name for column of predicted clusters in predictions. predictions DataFrame produced by the model’s transform method. trainingCost K-means cost (sum of squared distances to the nearest centroid for all points in the training dataset).

Attributes Documentation

cluster

DataFrame of predicted cluster centers for each training data point.

clusterSizes

Size of (number of data points in) each cluster.

featuresCol

Name for column of features in predictions.

k

The number of clusters the model was trained with.

numIter

Number of iterations.

predictionCol

Name for column of predicted clusters in predictions.

predictions

DataFrame produced by the model’s transform method.

trainingCost

K-means cost (sum of squared distances to the nearest centroid for all points in the training dataset). This is equivalent to sklearn’s inertia.