15.3 Model Training Process
- Train each base model using the same seed (
seed = 123), fold assignment (fold_assignment = "Modulo") and saving the cross-validated predictions (keep_cross_validation_predictions = TRUE).
# Regularized regression base learner
best_glm <- h2o.glm(
x = X,
y = Y,
training_frame = train_h2o,
alpha = 0.1,
remove_collinear_columns = TRUE,
nfolds = 10,
fold_assignment = "Modulo",
keep_cross_validation_predictions = TRUE,
seed = 123
)##
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|======================================================================| 100%# Random forest base learner
best_rf <- h2o.randomForest(
x = X,
y = Y,
training_frame = train_h2o,
ntrees = 1000,
mtries = 20,
max_depth = 30,
min_rows = 1,
sample_rate = 0.8,
nfolds = 10,
fold_assignment = "Modulo",
keep_cross_validation_predictions = TRUE,
seed = 123,
stopping_rounds = 50,
stopping_metric = "RMSE",
stopping_tolerance = 0
)## Warning in .h2o.processResponseWarnings(res): early stopping is enabled but neither score_tree_interval or score_each_iteration are defined. Early stopping will not be reproducible!.##
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|======================================================================| 100%# GBM base learner
best_gbm <- h2o.gbm(
x = X,
y = Y,
training_frame = train_h2o,
ntrees = 5000,
learn_rate = 0.01,
max_depth = 7,
min_rows = 5,
sample_rate = 0.8,
nfolds = 10,
fold_assignment = "Modulo",
keep_cross_validation_predictions = TRUE,
seed = 123,
stopping_rounds = 50,
stopping_metric = "RMSE",
stopping_tolerance = 0
)## Warning in .h2o.processResponseWarnings(res): early stopping is enabled but neither score_tree_interval or score_each_iteration are defined. Early stopping will not be reproducible!.##
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|======================================================================| 100%# XGBoost base learner
# We cannot run this under Windows- Set up the ensemble by
- Defining a list \(L\) of tuned based learners
- Defining a metalearner algorithm
# Stacked model
ensemble_tree <- h2o.stackedEnsemble(
x = X,
y = Y,
training_frame = train_h2o,
model_id = "my_tree_ensemble",
base_models = list(best_glm, best_rf, best_gbm),
# Meta learner: random forest
metalearner_algorithm = "drf"
)- Explore models correlations
ModelList <- list(
glm = best_glm,
rf = best_rf,
gbm = best_gbm
)
extract_cv <- function(x){
x@model$cross_validation_holdout_predictions_frame_id$name |>
h2o.getFrame() |>
as.vector()
}
lapply(ModelList, extract_cv) |>
as.data.frame() |>
cor()## glm rf gbm
## glm 1.0000000 0.9421599 0.9346891
## rf 0.9421599 1.0000000 0.9919788
## gbm 0.9346891 0.9919788 1.0000000As we can see the models show very similar results, so this case isn’t the best one to perform a stacking model.
- Comparing model performance
# Defining the rmse function
get_rmse <- function(model) {
results <- h2o.performance(model, newdata = test_h2o)
results@metrics$RMSE
}
# Get results from base learners
BaseLearnersRmse <- sapply(ModelList, get_rmse)
BaseLearnersRmse## glm rf gbm
## 30018.69 22247.60 20105.70# Stacked results
StackRmse <- get_rmse(ensemble_tree)
StackRmse## [1] 21300.69# Ratio
StackRmse/BaseLearnersRmse## glm rf gbm
## 0.7095810 0.9574378 1.0594352