如何获得H2o深度学习多项式模型的精度

Question

当我选择dl_model.show((时，它会显示所有信息，但不会显示模型的准确性，也不会显示验证数据的性能AUC。当我运行这个命令时，我得到了这个错误

print('AUC', dl_model.auc(valid = False))

KeyError                    Traceback (most recent call last)
<ipython-input-655-a4a2f0946c88> in <module>()
----> 1 print('AUC', dl_model.auc())
~Anaconda3libsite-packagesh2omodelmodel_base.py in auc(self, train, valid, xval)
682         tm = ModelBase._get_metrics(self, train, valid, xval)
683         m = {}
--> 684         for k, v in viewitems(tm): m[k] = None if v is None else v.auc()
685         return list(m.values())[0] if len(m) == 1 else m
686 
~Anaconda3libsite-packagesh2omodelmetrics_base.py in auc(self)
165     def auc(self):
166         """The AUC for this set of metrics."""
--> 167         return self._metric_json['AUC']
168 
169     def pr_auc(self):
KeyError: 'auc'

感谢

Answer 1

通常，如果您没有看到AUC度量，那是因为H2O Algo没有解决二进制分类问题。

如果你想要多项式问题的精度，使用[max_hit_ratio_k][1]并查看k=1。

如果您想查看多项式的一般度量，请检查文档中可用的内容，例如混淆矩阵和mean_per_class_error都可用。

请在下面找到一个例子：目标是获得hit_ratio k=1(见最后几行(

import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
h2o.init()
# import the iris dataset:
# this dataset is used to classify the type of iris plant
# the original dataset can be found at https://archive.ics.uci.edu/ml/datasets/Iris
iris = h2o.import_file("http://h2o-public-test-data.s3.amazonaws.com/smalldata/iris/iris_wheader.csv")
# convert response column to a factor
iris['class'] = iris['class'].asfactor()
# set the predictor names and the response column name
predictors = iris.columns[:-1]
response = 'class'
# split into train and validation sets
train, valid = iris.split_frame(ratios = [.8])
# try using the `link` parameter:
# Initialize and train a GLM
iris_glm = H2OGeneralizedLinearEstimator(family = 'multinomial', link = 'family_default')
iris_glm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)
pd = iris_glm.hit_ratio_table().as_data_frame()
pd.loc[(0,'hit_ratio')]