exclude_algos¶
- Available in: AutoML
- Hyperparameter: no
Description¶
This option allows you to specify a list of algorithms that should not be included in an AutoML run during the model-building phase. This option defaults to None/Null, which means that all algorithms are included. However, if the include_algos option is used, then the AutoML run will include only those specified algorithms. Note that these two options cannot both be specified.
The algorithms that can be specified include:
- DRF (including both the Random Forest and Extremely Randomized Trees (XRT) models)
- GLM
- XGBoost (XGBoost GBM)
- GBM (H2O GBM)
- DeepLearning (Fully-connected multi-layer artificial neural network)
- StackedEnsemble
Example¶
library(h2o)
h2o.init()
# Import a sample binary outcome training set into H2O
train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
# Identify predictors and response
x <- setdiff(names(train), y)
y <- "response"
# For binary classification, response should be a factor
train[,y] <- as.factor(train[,y])
# Train AutoML, omitting DeepLearning and DRF
aml <- h2o.automl(x = x, y = y,
training_frame = train,
max_runtime_secs = 30,
sort_metric = "logloss",
exclude_algos = c("DeepLearning", "DRF"))
# View the AutoML Leaderboard
lb <- aml@leaderboard
lb
model_id auc logloss
1 StackedEnsemble_AllModels_AutoML_20190321_095825 0.7866967 0.5550255
2 StackedEnsemble_BestOfFamily_AutoML_20190321_095825 0.7848515 0.5569458
3 XGBoost_1_AutoML_20190321_095825 0.7846668 0.5578654
4 XGBoost_2_AutoML_20190321_095825 0.7820392 0.5586830
5 GLM_grid_1_AutoML_20190321_095825_model_1 0.6826481 0.6385205
mean_per_class_error rmse mse
1 0.3309041 0.4338530 0.1882284
2 0.3231440 0.4346720 0.1889397
3 0.3324049 0.4349659 0.1891953
4 0.3269806 0.4356756 0.1898132
5 0.3972341 0.4726827 0.2234290
[5 rows x 6 columns]
import h2o
from h2o.automl import H2OAutoML
h2o.init()
# Import a sample binary outcome training set into H2O
train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv")
# Identify predictors and response
x = train.columns
y = "response"
x.remove(y)
# For binary classification, response should be a factor
train[y] = train[y].asfactor()
# Train AutoML, omitting DeepLearning and DRF
aml = H2OAutoML(max_runtime_secs = 30, sort_metric = "logloss",
exclude_algos = ["DeepLearning", "DRF"])
aml.train(x = x, y = y, training_frame = train)
# View the AutoML Leaderboard
lb = aml.leaderboard
lb
model_id auc logloss mean_per_class_error rmse mse
-------------------------------------------------- -------- --------- ---------------------- -------- --------
DRF_1_AutoML_20190321_100107 0.744882 0.597348 0.360293 0.452093 0.204388
XRT_1_AutoML_20190321_095341 0.741603 0.60012 0.342847 0.453342 0.205519
XRT_1_AutoML_20190321_100107 0.740636 0.600695 0.356075 0.453646 0.205795
DRF_1_AutoML_20190321_095341 0.740674 0.60294 0.375423 0.453271 0.205454
DeepLearning_grid_1_AutoML_20190321_095341_model_1 0.711473 0.620394 0.387857 0.463987 0.215284
DeepLearning_1_AutoML_20190321_100107 0.703753 0.628472 0.401192 0.467294 0.218363
GLM_grid_1_AutoML_20190321_095341_model_1 0.682648 0.63852 0.397234 0.472683 0.223429
GLM_grid_1_AutoML_20190321_100107_model_1 0.682648 0.63852 0.397234 0.472683 0.223429
DeepLearning_1_AutoML_20190321_095341 0.684733 0.639195 0.418683 0.472425 0.223185
DeepLearning_grid_1_AutoML_20190321_100107_model_1 0.670713 0.643133 0.434458 0.475507 0.226107
[10 rows x 6 columns]