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Getting Individual Metrics From H2O Model in Python [Code Snippets]

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Getting Individual Metrics From H2O Model in Python [Code Snippets]

Learn about creating a test data frame to run the H2O deep learning algorithm and collecting individual model metrics based on training and/or validation data.

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You can get individual model metrics for your model based on training and/or validation data. Here is the code snippets.

Below, I am creating a test data frame to run the H2O deep learning algorithm and then showing how to collect individual model metrics based on training and/or validation data.

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))

Note: As I did not pass the validation frame, I set valid = False to get training metrics. If you have passed validation metrics then you can set valid = True to get validation metrics as well.

If you want to see what is inside model object, you can look at the JSON object below:

model.get_params()

That's it. Enjoy!

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Topics:
metrics ,h2o ,python ,ai ,deep learning ,algorithm ,data analytics

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