pyspark.ml.clustering.KMeansSummary(java_obj: Optional[JavaObject] = None)¶
Summary of KMeans.
Attributes
DataFrame of predicted cluster centers for each training data point.
Size of (number of data points in) each cluster.
Name for column of features in predictions.
The number of clusters the model was trained with.
Number of iterations.
Name for column of predicted clusters in predictions.
DataFrame produced by the modelâs transform method.
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 modelâs 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 sklearnâs inertia.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4