GMM
Gaussian Mixture Model
| Param name | Type(s) | Default | Description |
|---|---|---|---|
| featuresCol | Vector | "features" | Feature vector |
| Param name | Type(s) | Default | Description |
|---|---|---|---|
| predictionCol | Int | "prediction" | Predicted cluster center |
| probabilityCol | Vector | "probability" | Probability of each cluster |
# Load training data
df <- read.df("data/mllib/sample_kmeans_data.txt", source = "libsvm")
training <- df
test <- df
# Fit a gaussian mixture clustering model with spark.gaussianMixture
model <- spark.gaussianMixture(training, ~ features, k = 2)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, test)
showDF(predictions)