R H2O 网格搜索：如何在新数据上训练顶级模型？

Question

在运行超参数搜索并从网格中提取最佳模型后，是否可以使用模型对象在新数据集上进行训练？我现在看到的唯一方法是使用最佳模型的参数手动创建对训练函数（例如 h2o.gbm()）的调用，但这非常麻烦。

Answer 1

checkpoint 参数可以满足您的需求，它可以从原始模型进一步训练模型。

此功能适用于h2o 包中的gbm、random forest 和deep learning。

下面的示例代码复制自：http://s3.amazonaws.com/h2o-release/h2o/master/3689/docs-website/h2o-docs/data-science/algo-params/checkpoint.html

library(h2o)
h2o.init()

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

# set the predictor names and the response column name
predictors <- c("displacement","power","weight","acceleration","year")
response <- "economy_20mpg"

# split into train and validation sets
cars.split <- h2o.splitFrame(data = cars,ratios = 0.8, seed = 1234)
train <- cars.split[[1]]
valid <- cars.split[[2]]

# build a GBM with 1 tree (ntrees = 1) for the first model:
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    validation_frame = valid, ntrees = 1, seed = 1234)

# print the auc for the validation data
print(h2o.auc(cars_gbm, valid = TRUE))

# re-start the training process on a saved GBM model using the ‘checkpoint‘ argument:
# the checkpoint argument requires the model id of the model on which you wish to continue building
# get the model's id from "cars_gbm" model using `cars_gbm@model_id`
# the first model has 1 tree, let's continue building the GBM with an additional 49 more trees, so set ntrees = 50

# to see how many trees the original model built you can look at the `ntrees` attribute
print(paste("Number of trees built for cars_gbm model:", cars_gbm@allparameters$ntrees))

# build and train model with 49 additional trees for a total of 50 trees:
cars_gbm_continued <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    validation_frame = valid, checkpoint = cars_gbm@model_id, ntrees = 50, seed = 1234)

# print the auc for the validation data
print(h2o.auc(cars_gbm_continued, valid = TRUE))

# you can also use checkpointing to pass in a new dataset (see options above for parameters you cannot change)
# simply change out the training and validation frames with your new dataset

编辑（基于下面@Edward 的评论：）

h2o.grid会返回一系列模型，你可以得到最好的模型处理。所有参数都保存在模型手柄中，然后您可以将参数应用到新模型中。

grid <- h2o.getGrid(h2o.grid@grid_id,sort_by = "auc",decreasing=TRUE)
model.h2o <- h2o.getModel(grid@model_ids[[1]])

model@allparameters 包含所有使用的参数，您可以使用这些参数来创建新模型和新数据。