library(h2o)
## 
## ----------------------------------------------------------------------
## 
## Your next step is to start H2O:
##     > h2o.init()
## 
## For H2O package documentation, ask for help:
##     > ??h2o
## 
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
## 
## ----------------------------------------------------------------------
## 
## Attaching package: 'h2o'
## The following objects are masked from 'package:stats':
## 
##     cor, sd, var
## The following objects are masked from 'package:base':
## 
##     &&, %*%, %in%, ||, apply, as.factor, as.numeric, colnames,
##     colnames<-, ifelse, is.character, is.factor, is.numeric, log,
##     log10, log1p, log2, round, signif, trunc
h2o.init()
##  Connection successful!
## 
## R is connected to the H2O cluster: 
##     H2O cluster uptime:         1 minutes 57 seconds 
##     H2O cluster timezone:       Europe/Prague 
##     H2O data parsing timezone:  UTC 
##     H2O cluster version:        3.37.0.99999 
##     H2O cluster version age:    4 minutes  
##     H2O cluster name:           tomasfryda 
##     H2O cluster total nodes:    1 
##     H2O cluster total memory:   1.74 GB 
##     H2O cluster total cores:    16 
##     H2O cluster allowed cores:  8 
##     H2O cluster healthy:        TRUE 
##     H2O Connection ip:          localhost 
##     H2O Connection port:        54321 
##     H2O Connection proxy:       NA 
##     H2O Internal Security:      FALSE 
##     R Version:                  R version 4.1.3 (2022-03-10)
h2o.no_progress()
df <- h2o.importFile("https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv")

response <- "quality"

predictors <- c(
  "fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide",
  "total sulfur dioxide", "density", "pH", "sulphates", "alcohol",  "type"
)


df_splits <- h2o.splitFrame(df, seed = 1)
train <- df_splits[[1]]
test <- df_splits[[2]]
aml <- h2o.automl(predictors, response, train, max_runtime_secs = 120)
h2o.explain(aml, test)

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the newdata. At most 20 models are shown by default.

model_id rmse mse mae rmsle mean_residual_deviance training_time_ms predict_time_per_row_ms algo
1 StackedEnsemble_AllModels_3_AutoML_1_20220408_154334 0.617685776617159 0.381535718635142 0.438378587069498 0.0936590959349768 0.381535718635142 478 0.039631 StackedEnsemble
2 StackedEnsemble_AllModels_2_AutoML_1_20220408_154334 0.618013024931484 0.381940098984963 0.439959229672551 0.0937501017812696 0.381940098984963 356 0.019012 StackedEnsemble
3 StackedEnsemble_AllModels_1_AutoML_1_20220408_154334 0.618214658361304 0.382189363812783 0.440410150437152 0.0937798961585461 0.382189363812783 344 0.012239 StackedEnsemble
4 StackedEnsemble_BestOfFamily_3_AutoML_1_20220408_154334 0.618930632968796 0.383075128427154 0.441219995671507 0.0938838970587147 0.383075128427154 469 0.016097 StackedEnsemble
5 StackedEnsemble_BestOfFamily_2_AutoML_1_20220408_154334 0.619252731243278 0.38347394515226 0.442115525875666 0.0939366793118372 0.38347394515226 368 0.013446 StackedEnsemble
6 StackedEnsemble_BestOfFamily_4_AutoML_1_20220408_154334 0.620728928088902 0.385304402166397 0.443204693679439 0.094117617659717 0.385304402166397 242 0.02157 StackedEnsemble
7 DRF_1_AutoML_1_20220408_154334 0.623399620739062 0.388627087137606 0.451090313693956 0.0946911453486115 0.388627087137606 3195 0.005967 DRF
8 XRT_1_AutoML_1_20220408_154334 0.62606109981841 0.391952500705837 0.453111049733941 0.0951338672138981 0.391952500705837 2223 0.006284 DRF
9 GBM_grid_1_AutoML_1_20220408_154334_model_7 0.637915265136001 0.406935885493535 0.476742598832093 0.0964849953561878 0.406935885493535 649 0.006073 GBM
10 GBM_grid_1_AutoML_1_20220408_154334_model_6 0.641354902962878 0.411336111554523 0.476741007078573 0.0969221694646524 0.411336111554523 626 0.005926 GBM
11 GBM_grid_1_AutoML_1_20220408_154334_model_9 0.642958284406168 0.413395355486523 0.476448028841323 0.0975142896460704 0.413395355486523 601 0.002098 GBM
12 XGBoost_grid_1_AutoML_1_20220408_154334_model_10 0.648563761818005 0.420634953143521 0.47752383277203 0.0978531204988241 0.420634953143521 1142 0.00208 XGBoost
13 GBM_grid_1_AutoML_1_20220408_154334_model_5 0.648599756724984 0.420681644423708 0.483004603881046 0.0980326905339133 0.420681644423708 825 0.004597 GBM
14 GBM_4_AutoML_1_20220408_154334 0.649691920305335 0.422099591310033 0.487717334731695 0.0982314888081577 0.422099591310033 878 0.004574 GBM
15 StackedEnsemble_BestOfFamily_1_AutoML_1_20220408_154334 0.650043181849592 0.422556138269142 0.486853519201754 0.0981231711888431 0.422556138269142 576 0.007308 StackedEnsemble
16 GBM_grid_1_AutoML_1_20220408_154334_model_3 0.650982115890274 0.423777715208978 0.4882610338403 0.0986011433390065 0.423777715208978 566 0.005961 GBM
17 GBM_grid_1_AutoML_1_20220408_154334_model_4 0.653741189108367 0.427377542336821 0.496609192879076 0.0986346994882898 0.427377542336821 606 0.005631 GBM
18 XGBoost_grid_1_AutoML_1_20220408_154334_model_2 0.654850868750006 0.428829660302637 0.465980194210394 0.0992954680663143 0.428829660302637 2734 0.001862 XGBoost
19 GBM_3_AutoML_1_20220408_154334 0.655021303157682 0.429052907590388 0.498592026724169 0.0988840102934742 0.429052907590388 792 0.004217 GBM
20 XGBoost_grid_1_AutoML_1_20220408_154334_model_5 0.658951956027864 0.434217680352948 0.45547348612504 0.099599420783599 0.434217680352948 1615 0.001332 XGBoost

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see “striped” lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

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.