Description Usage Arguments Value
Autotrain implements H2O grid search to automatically build machine learning models
1 2 3 |
train |
H2O frame object containing labeled data for model training. No Default. |
valid |
H2O frame object containing labeled data for model validation. No Default. |
y |
Character object of length 1 identifying the column name of the target variable. No Default. |
x |
Character object of length 1 or more identifying the column name(s) of the input variables. No Default. |
algorithms |
Character object of length 3, 2, or 1, specifying which alrogrithms to automatically train. The autotrain function will run a separate grid search for each algorimth type. Choices are: "deeplearning", "randomForest", and "gbm" following the naming convention in H2O version 3. Defaults to c("deeplearning", "randomForest", "gbm"). |
eval_metric |
Character object defining evaluation metric for training. Defualt is "AUTO" and uses built-in H2O automatic choice for target data type. |
validation_type |
Defines validation type for training models. Defaults to "shared_holdout" indicating all model built with all algorithms share the same validation set. Currently, this is the only option in autotrain. Planned types include "random_holdout" where each model will get a unique randomized sample of labeled data for validation, and "xval" in which the cross validation functionality in H2O will be implemented in every model. |
runtime_secs |
Character Object which sets the length of time each grid search will run. Defaults to 20, thus the default runtime is 20 sec * (length of algorimths) = 1 minute. |
wd |
Character object defining file path where resulting modeling will be saved. Defualts to current working directory. |
List object containing H2O model objects
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