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balanced-random-forest-classifier

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hanfei1986 / Comparison-between-RandomForestClassifier-and-BalancedRandomForestClassifier

Imbalanced data commonly exist in real world, especially in anamoly-detection tasks. Handling imbalanced data is important to the tasks, otherwise the predictions are biased towards the majority class. BalancedRandomForestClassifier can deal with the imbalanced data without knowing any novel techniques like SMOTE.

machine-learning imbalanced-data random-forest-classifier balanced-random-forest-classifier

Updated Aug 27, 2023
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MariiaSam / Stroke-Prediction

A model for predicting the risk of stroke in a patient

numpy scikit-learn seaborn logistic-regression matplotlib gradient-boosting imbalanced-learning random-forest-classifier joblib streamlit decission-tree-classifier balanced-random-forest-classifier

Updated Mar 23, 2025
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