<p>您可以根据培训和/或验证数据获取模型的一些单独模型度量。以下是代码片段:</p>
<pre><code>import h2o
h2o.init(strict_version_check= False , port = 54345)
from h2o.estimators.deeplearning import H2ODeepLearningEstimator
model = H2ODeepLearningEstimator()
rows = [[1,2,3,4,0], [2,1,2,4,1], [2,1,4,2,1], [0,1,2,34,1], [2,3,4,1,0]] * 50
fr = h2o.H2OFrame(rows)
X = fr.col_names[0:4]
## Classification Model
fr[4] = fr[4].asfactor()
model.train(x=X, y="C5", training_frame=fr)
print('Model Type:', model.type)
print('logloss', model.logloss(valid = False))
print('Accuracy', model.accuracy(valid = False))
print('AUC', model.auc(valid = False))
print('R2', model.r2(valid = False))
print('RMSE', model.rmse(valid = False))
print('Error', model.error(valid = False))
print('MCC', model.mcc(valid = False))
## Regression Model
fr = h2o.H2OFrame(rows)
model.train(x=X, y="C5", training_frame=fr)
print('Model Type:', model.type)
print('R2', model.r2(valid = False))
print('RMSE', model.rmse(valid = False))
</code></pre>
<p>注意:由于我没有通过验证框架,所以我设置valid=False来获取培训指标。如果您通过了验证指标,那么您可以设置valid=True来获取验证指标。在</p>
<p>如果您想查看模型对象内部是什么,可以查看json对象,如下所示:</p>
^{pr2}$