Question

我在H2O R中运行分类模型。我想为我的训练数据集提取拟合的模型预测。

代码：

train <- as.h2o(train)
test <- as.h2o(test)
y <- "class"
x <- setdiff(names(train), y)
family <- "multinomial"
nfolds <- 5 
gbm1 <- h2o.gbm(x = x, y = y, distribution = family,
            training_frame = train,
            seed = 1,
            nfolds = nfolds,
            fold_assignment = "Modulo",
            keep_cross_validation_predictions = TRUE)
h2o.getFrame(gbm1@model$cross_validation_predictions[[gbm1@allparameters$nfolds]]$name)[,2:4]

Answer 1

以下是如何从R中训练的H2O模型中提取交叉验证预测的简单示例（使用Iris数据集）。

library(h2o)
h2o.init(nthreads = -1)

data(iris)
train <- as.h2o(iris)
y <- "Species"
x <- setdiff(names(train), y)
family <- "multinomial"
nfolds <- 5 

gbm1 <- h2o.gbm(x = x, y = y, 
                distribution = family,
                training_frame = train,
                seed = 1,
                nfolds = nfolds,
                fold_assignment = "Modulo",
                keep_cross_validation_predictions = TRUE)

cvpreds_id <- gbm1@model$cross_validation_holdout_predictions_frame_id$name
cvpreds <- h2o.getFrame(cvpreds_id)

cvpreds对象是一个H2OFrame，如下所示：

> cvpreds
  predict    setosa   versicolor    virginica
1  setosa 0.9986012 0.0008965135 0.0005022631
2  setosa 0.9985695 0.0004486762 0.0009818434
3  setosa 0.9981387 0.0004777671 0.0013835724
4  setosa 0.9985246 0.0006259377 0.0008494549
5  setosa 0.9989924 0.0005033832 0.0005042294
6  setosa 0.9981410 0.0013581692 0.0005008536

[150 rows x 4 columns]

R - H2O-我如何获得训练有素的模型预测/概率？

1 个答案: