Comments (2)
// later, in another process const classifier = RandomForestClassifier.load(JSON.parse(serialized)); const result = classifier.predict(data);
Awesome work guys. Thank you!
from random-forest.
Hello! Yes, a model should be serializable to JSON:
const classifier = new RandomForestClassifier(options);
classifier.train(trainingSet, predictions);
const serialized = JSON.stringify(classifier);
// later, in another process
const classifier = RandomForestClassifier.load(JSON.parse(serialized));
const result = classifier.predict(data);
from random-forest.
Related Issues (20)
- Poor accuracy HOT 8
- RandomForestBase load showing error HOT 3
- Inconsistent array dimensions HOT 5
- Feature Importance HOT 5
- Default seed makes a random forest generate same estimators HOT 2
- Feature Bagging Options HOT 1
- 'ml-random-forest' is not in the npm registry HOT 1
- New feature: max_samples option for sample bootstrapping HOT 1
- Load model that was saved as JSON HOT 1
- Cannot load the saved Model HOT 1
- Quantile estimation
- Regression example gives weird results since 2.0.0 HOT 2
- types.d.ts file is causing compile error
- TODO before release HOT 5
- Export `RandomForestBaseOptions` type HOT 2
- Column indices are out of range HOT 1
- Add logging and verbosity level
- Problem of accuracy HOT 3
- Random Forest Regression error 'input must not be empty' when fed text frequency array training set HOT 14
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from random-forest.