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AutoH2oGBMClassifier is an automated H2O modeling framework with grid-tuning and model evaluation that runs a variety of steps. First, a stratified sampling (by the target variable) is done to create train and validation sets. Then, the function will run a random grid tune over N number of models and find which model is the best (a default model is always included in that set). Once the model is identified and built, several other outputs are generated: validation data with predictions, evaluation plot, evaluation metrics, variable importance, partial dependence calibration plots, and column names used in model fitting.
AutoH2oGBMClassifier(data, ValidationData = NULL, TestData = NULL,
TargetColumnName = NULL, FeatureColNames = NULL,
eval_metric = "auc", Trees = 50, GridTune = FALSE,
MaxMem = "32G", NThreads = max(1, parallel::detectCores() - 2),
MaxModelsInGrid = 2, model_path = NULL, ModelID = "FirstModel",
NumOfParDepPlots = 3, ReturnModelObjects = TRUE,
SaveModelObjects = FALSE, IfSaveModel = "mojo",
H2OShutdown = FALSE)This is your data set for training and testing your model
This is your holdout data set used in modeling either refine your hyperparameters.
This is your holdout data set. Catboost using both training and validation data in the training process so you should evaluate out of sample performance with this data set.
Either supply the target column name OR the column number where the target is located (but not mixed types). Note that the target column needs to be a 0 | 1 numeric variable.
Either supply the feature column names OR the column number where the target is located (but not mixed types)
This is the metric used to identify best grid tuned model. Choose from "AUC" or "logloss"
The maximum number of trees you want in your models
Set to TRUE to run a grid tuning procedure. Set a number in MaxModelsInGrid to tell the procedure how many models you want to test.
Set the maximum amount of memory you'd like to dedicate to the model run. E.g. "32G"
Set to the number of threads you want to use for running this function
Number of models to test from grid options (1080 total possible options)
A character string of your path file to where you want your output saved
A character string to name your model and output
Tell the function the number of partial dependence calibration plots you want to create.
Set to TRUE to output all modeling objects (E.g. plots and evaluation metrics)
Set to TRUE to return all modeling objects to your environment
Set to "mojo" to save a mojo file, otherwise "standard" to save a regular H2O model object
Set to TRUE to shut down H2O after running the function
Saves to file and returned in list: VariableImportance.csv, Model, ValidationData.csv, EvalutionPlot.png, EvaluationMetrics.csv, ParDepPlots.R a named list of features with partial dependence calibration plots, GridCollect, and GridList
Other Automated Binary Classification: AutoCatBoostClassifier,
AutoH2oDRFClassifier,
AutoXGBoostClassifier
# NOT RUN {
Correl <- 0.85
N <- 1000
data <- data.table::data.table(Target = runif(N))
data[, x1 := qnorm(Target)]
data[, x2 := runif(N)]
data[, Independent_Variable1 := log(pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable2 := (pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable3 := exp(pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable4 := exp(exp(pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2))))]
data[, Independent_Variable5 := sqrt(pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))]
data[, Independent_Variable6 := (pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))^0.10]
data[, Independent_Variable7 := (pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))^0.25]
data[, Independent_Variable8 := (pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))^0.75]
data[, Independent_Variable9 := (pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))^2]
data[, Independent_Variable10 := (pnorm(Correl * x1 +
sqrt(1-Correl^2) * qnorm(x2)))^4]
data[, Independent_Variable11 := as.factor(
ifelse(Independent_Variable2 < 0.20, "A",
ifelse(Independent_Variable2 < 0.40, "B",
ifelse(Independent_Variable2 < 0.6, "C",
ifelse(Independent_Variable2 < 0.8, "D", "E")))))]
data[, ':=' (x1 = NULL, x2 = NULL)]
data[, Target := as.factor(ifelse(Independent_Variable2 < 0.5, 1, 0))]
TestModel <- AutoH2oGBMClassifier(data,
ValidationData = NULL,
TestData = NULL,
TargetColumnName = "Target",
FeatureColNames = 2:ncol(data),
eval_metric = "auc",
Trees = 50,
GridTune = FALSE,
MaxMem = "32G",
NThreads = max(1, parallel::detectCores()-2),
MaxModelsInGrid = 10,
model_path = NULL,
ModelID = "FirstModel",
NumOfParDepPlots = 3,
ReturnModelObjects = TRUE,
SaveModelObjects = FALSE,
IfSaveModel = "mojo",
H2OShutdown = FALSE)
# }
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