h2o (version 3.44.0.3)

h2o.model_correlation_heatmap: Model Prediction Correlation Heatmap

Description

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering).

Usage

h2o.model_correlation_heatmap(
  object,
  newdata,
  top_n = 20,
  cluster_models = TRUE,
  triangular = TRUE
)

Value

A ggplot2 object.

Arguments

object

A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard).

newdata

An H2O Frame. Predictions from the models will be generated using this frame, so this should be a holdout set.

top_n

Integer specifying the number models shown in the heatmap (used only with an AutoML object, and based on the leaderboard ranking. Defaults to 20.

cluster_models

Logical. Order models based on their similarity. Defaults to TRUE.

triangular

Print just the lower triangular part of correlation matrix. Defaults to TRUE.

Examples

if (FALSE) {
library(h2o)
h2o.init()

# Import the wine dataset into H2O:
f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv"
df <-  h2o.importFile(f)

# Set the response
response <- "quality"

# Split the dataset into a train and test set:
splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1)
train <- splits[[1]]
test <- splits[[2]]

# Build and train the model:
aml <- h2o.automl(y = response,
                  training_frame = train,
                  max_models = 10,
                  seed = 1)

# Create the model correlation heatmap
model_correlation_heatmap <- h2o.model_correlation_heatmap(aml, test)
print(model_correlation_heatmap)
}