Variable Importance Heatmap across multiple models
Variable Importance Heatmap across multiple models
Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.
object: A list of H2O models, an H2O AutoML instance, or an H2OFrame with a 'model_id' column (e.g. H2OAutoML leaderboard).
top_n: Integer specifying the number models shown in the heatmap (based on leaderboard ranking). Defaults to 20.
num_of_features: Integer specifying the number of features shown in the heatmap based on the maximum variable importance across the models. Use NULL for unlimited. Defaults to 20.
Returns
A ggplot2 object.
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 responseresponse <-"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 variable importance heatmapvarimp_heatmap <- h2o.varimp_heatmap(aml)print(varimp_heatmap)## End(Not run)