``max_models``
--------------
- Available in: AutoML
- Hyperparameter: no
Description
~~~~~~~~~~~
Use this option to specify the maximum number of models to build in the AutoML run, excluding the Stacked Ensemble models. This option defaults to Null/None.
Related Parameters
~~~~~~~~~~~~~~~~~~
- `max_runtime_secs <max_runtime_secs.html>`__
- `max_runtime_secs_per_model <max_runtime_secs_per_model.html>`__
Example
~~~~~~~
.. example-code::
.. code-block:: r
library(h2o)
h2o.init()
# Import the prostate dataset
prostate <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip")
# Set the predictor names and the response column name
y <- "CAPSULE"
x <- setdiff(names(prostate), c(p_y, "ID"))
# Train AutoML
aml <- h2o.automl(x = x,
y = y,
training_frame = prostate,
seed = 1234,
max_models = 5,
max_runtime_secs = 200,
max_runtime_secs_per_model = 40)
# View the AutoML Leaderboard
lb <- aml@leaderboard
lb
model_id mean_residual_deviance
1 StackedEnsemble_BestOfFamily_AutoML_20190321_110032 0.0009730593
2 StackedEnsemble_AllModels_AutoML_20190321_110032 0.0009730593
3 DRF_1_AutoML_20190321_110032 0.0012766064
4 XRT_1_AutoML_20190321_110032 0.0038347775
5 XGBoost_2_AutoML_20190321_110032 0.0064206276
6 XGBoost_1_AutoML_20190321_110032 0.0544174809
rmse mse mae rmsle
1 0.03119390 0.0009730593 0.02086672 0.02368844
2 0.03119390 0.0009730593 0.02086672 0.02368844
3 0.03572963 0.0012766064 0.01406001 0.02661268
4 0.06192558 0.0038347775 0.03330358 0.04958889
5 0.08012882 0.0064206276 0.06873394 0.06112533
6 0.23327555 0.0544174809 0.18390358 0.16640402
[7 rows x 6 columns]
.. code-block:: python
import h2o
from h2o.automl import H2OAutoML
h2o.init()
# Import a sample binary outcome training set into H2O
prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip")
# Set the predictor names and the response column name
response = "CAPSULE"
predictor = prostate.names[2:9]
# Train AutoML
aml = H2OAutoML(max_models = 5,
max_runtime_secs = 200,
max_runtime_secs_per_model = 40,
seed = 1234)
aml.train(x = predictor, y = response, training_frame = prostate)
# View the AutoML Leaderboard
lb = aml.leaderboard
lb
model_id mean_residual_deviance rmse mse mae rmsle
--------------------------------------------------- ------------------------ --------- ----------- --------- ---------
StackedEnsemble_AllModels_AutoML_20190321_111608 0.000282073 0.016795 0.000282073 0.0103226 0.0129982
StackedEnsemble_BestOfFamily_AutoML_20190321_111608 0.000282073 0.016795 0.000282073 0.0103226 0.0129982
DRF_1_AutoML_20190321_111608 0.000334287 0.0182835 0.000334287 0.0076525 0.0140754
XRT_1_AutoML_20190321_111608 0.0015397 0.039239 0.0015397 0.0217268 0.0293752
XGBoost_2_AutoML_20190321_111608 0.0118094 0.108671 0.0118094 0.0888375 0.0804565
XGBoost_1_AutoML_20190321_111608 0.0675672 0.259937 0.0675672 0.213536 0.184793
GLM_grid_1_AutoML_20190321_111608_model_1 0.193551 0.439944 0.193551 0.397327 0.306996
[7 rows x 6 columns]