estimate_k

  • Available in: K-Means
  • Hyperparameter: yes

Description

This option is used to specify whether to estimate the number of clusters (\(<=k\)) iteratively (independent of the seed) and deterministically (beginning with \(k=1,2,3...\)). If enabled, for each \(k\) the estimate will go up to max_iterations.

Notes:

  • This option requires that at least one column includes numeric data. You will receive an error if your data has no numeric columns.
  • If this option is enabled and a seed is provided, the seed will be ignored unless you are performing cross validation.
  • This option cannot be used with user_points. You will receive an error during model training if you enable this option and specify user_points.

This option is disabled by default.

Example

library(h2o)
h2o.init()

# import the iris dataset:
# this dataset is used to classify the type of iris plant
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
iris <-h2o.importFile("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")

# convert response column to a factor
iris['class'] <-as.factor(iris['class'])

# set the predictor names
predictors <-colnames(iris)[-length(iris)]

# split into train and validation
iris_splits <- h2o.splitFrame(data = iris, ratios = .8, seed = 1234)
train <- iris_splits[[1]]
valid <- iris_splits[[2]]

# try using the `estimate_k` parameter:
# set k to the upper limit of classes you'd like to consider
# set standardize to False as well since the scales for each feature are very close
iris_kmeans <- h2o.kmeans(x = predictors, k = 10, estimate_k = T, standardize = F,
                          training_frame = train, validation_frame=valid, seed = 1234)

# print the model summary to see the number of clusters chosen
summary(iris_kmeans)

import h2o
from h2o.estimators.kmeans import H2OKMeansEstimator
h2o.init()

# import the iris dataset:
# this dataset is used to classify the type of iris plant
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")

# convert response column to a factor
iris['class'] = iris['class'].asfactor()

# set the predictor names
predictors = iris.columns[:-1]

# split into train and validation sets
train, valid = iris.split_frame(ratios = [.8], seed = 1234)

# try using the `estimate_k` parameter:
# set k to the upper limit of classes you'd like to consider
# set standardize to False as well since the scales for each feature are very close
# initialize the estimator then train the model
iris_kmeans = H2OKMeansEstimator(k = 10, estimate_k = True, standardize = False, seed = 1234)
iris_kmeans.train(x = predictors, training_frame = train, validation_frame=valid)

# print the model summary to see the number of clusters chosen
iris_kmeans.summary()