Data Imbalance Mitigation refers to techniques and strategies applied to address skewed or disproportionate distributions within datasets, which are particularly common in financial data where rare events, such as market crashes or specific trade types, are underrepresented. In crypto, this is crucial for training robust predictive models.
Mechanism
Mitigation methods often involve sampling techniques like oversampling minority classes, for instance using synthetic minority over-sampling technique (SMOTE), or undersampling majority classes. Additionally, cost-sensitive learning algorithms adjust the weight of observations during model training to prevent algorithms from overlooking critical but infrequent data patterns.
Methodology
The strategic goal is to build machine learning models that perform reliably even when confronted with infrequent yet significant events, essential for accurate risk assessment and fraud detection in crypto investing and smart trading. By ensuring balanced representation, models for institutional options or RFQ pricing exhibit improved predictive accuracy and reduced bias.
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