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Posted to dev@ignite.apache.org by "Alexey Zinoviev (Jira)" <ji...@apache.org> on 2019/11/26 17:07:00 UTC
[jira] [Created] (IGNITE-12396) [ML] Random Forest generates NaN
for a part of models on small datasets
Alexey Zinoviev created IGNITE-12396:
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Summary: [ML] Random Forest generates NaN for a part of models on small datasets
Key: IGNITE-12396
URL: https://issues.apache.org/jira/browse/IGNITE-12396
Project: Ignite
Issue Type: Bug
Components: ml
Affects Versions: 3.0
Reporter: Alexey Zinoviev
Assignee: Alexey Zinoviev
Fix For: 3.0
@Override public Double predict(Vector features) {
double[] predictions = new double[models.size()];
for (int i = 0; i < models.size(); i++)
predictions[i] = models.get(i).predict(features);
return predictionsAggregator.apply(predictions);
}
predictionAggreagtor gets a lot of models and part of them returns null and it could be aggregated, first of all handle this in Aggregator (using threshold for amount of broken models before aggregation) also RandomForest trees should return Double.NaN - it should fail or throw message after the training
I've tested with 100 or 1000 rows and it fails and doesn't fail on 10 000 rows
RF generates a few models with one LEAF node with empty val (Double.NaN by default)
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