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Neural network balanced weight for imbalanced classification

Neural Network Model Balanced Weight For Imbalanced Classification In Keras

When using a neural network model to classify imbalanced data, we can adjust the balanced weight for the cost function to give more attention to the minority class. Python’s Keras library has a built-in option called class_weight to help us achieve this quickly. One benefit of using the balanced weight adjustment is that we can use the …

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Isolation Forest For Anomaly Detection And Imbalanced Classification

Isolation Forest For Anomaly Detection

Isolation forest uses the number of tree splits to identify anomalies or minority classes in an imbalanced dataset. The idea is that anomaly data points take fewer splits because the density around the anomalies is low. Python’s sklearn library has an implementation for the isolation forest model. Isolation forest is an unsupervised algorithm, where the …

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