pyspark.ml.clustering.
KMeansSummary
Summary of KMeans.
New in version 2.1.0.
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
New in version 2.4.0.
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.