h2o.model_correlation function

Model Prediction Correlation

Model Prediction Correlation

Get a data.frame containing 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).

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

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: (DEPRECATED) 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.

Returns

A data.frame containing variable importance.

Examples

## Not run: 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 model_correlation <- h2o.model_correlation(aml, test) print(model_correlation) ## End(Not run)
  • Maintainer: Tomas Fryda
  • License: Apache License (== 2.0)
  • Last published: 2024-01-11