Project Scope
This Module help to select the base model over various ML Algorithms for our classification
Installation
pip3 install maxiverse (or) pip3 install maxiverse_(version)
Usage
>>> from maxiverse.classifier.evaluator import EvalModel
>>>
>>> EvalModel._has_classifier()
# ['logreg', 'svm', 'dtree', 'rnf', 'nvbys', 'adabst', 'knn', 'xgbst']
>>>
>>>
>>> EvalModel(X_train, Y_train, 10)._perform_validate(['knn'])
KNearestClassifier Best Score
Accuracy 0.978644 KNearest Classifier
Precision 0.992221 KNearest Classifier
Recall 0.964826 KNearest Classifier
F1 Score 0.978320 KNearest Classifier
>>>
>>> EvalModel(X_train, Y_train, 20)._perform_validate(['knn', 'rnf'])
KNearestClassifier Random Forest Best Score
Accuracy 0.978644 0.983600 RandomForest
Precision 0.992221 0.994982 RandomForest
Recall 0.964826 0.972086 RandomForest
F1 Score 0.978320 0.983391 RandomForest
Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
License
1 maintainer
Metadata
1 maintainer
Release files for maxiverse 0.0.5
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| File | Size | Uploaded | Provenance | Actions |
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Total release size: 8.7 kB
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Release files / maxiverse-0.0.5-py3-none-any.whl
| Download URL | maxiverse-0.0.5-py3-none-any.whl |
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| Tags | Python 3 |
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