sample_size¶
Available in: Isolation Forest, Extended Isolation Forest
Hyperparameter: yes
Description¶
This option specifies the number of randomly sampled observations used to train each Isolation Forest tree. For Isolation Forest, If set to -1, sample_rate will be used instead. This value defaults to 256 for both algorithms.
Example¶
library(h2o)
h2o.init()
# import the ecg discord datasets:
train <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv")
test <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_test.csv")
# train using the `sample_size` parameter:
isofor_model <- h2o.isolationForest(training_frame = train, sample_size = 5, ntrees = 7)
# test the prediction
anomaly_score <- h2o.predict(isofor_model, test)
anomaly_score
predict mean_length
1 -0.16666667 2.857143
2 -0.16666667 2.857143
3 -0.08333333 2.714286
4 0.16666667 2.285714
5 0.00000000 2.571429
6 0.33333333 2.000000
[23 rows x 2 columns]
import h2o
from h2o.estimators.isolation_forest import H2OIsolationForestEstimator
h2o.init()
# import the ecg discord datasets:
train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv")
test = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_test.csv")
# try using the `sample_size` parameter:
isofor_model = H2OIsolationForestEstimator(sample_size = 5, ntrees=7)
# then train your model
isofor_model.train(training_frame = train)
perf = isofor_model.model_performance()
perf
ModelMetricsAnomaly: isolationforest
** Reported on train data. **
Anomaly Score: 1.1392230576441102
Normalized Anomaly Score: 0.3631578947368421
test_pred = isofor_model.predict(test)
test_pred
predict mean_length
--------- -------------
-0.1 1.57143
-0.1 1.57143
0 1.42857
0 1.42857
0 1.42857
-0.1 1.57143
0.1 1.28571
0 1.42857
0.2 1.14286
-0.1 1.57143
[23 rows x 2 columns]