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Danica Simic on Instagram: "Here's how to actually evaluate your ML model Data leakage, overfitting and underfitting are real problems you'll face when training ML models, especially when data and features are unbalanced A rigorous process ensures your model generalizes and that reported metrics reflect real performance. Let's talk about it 1. Data Splitting Separate train, validation, and test sets to mirror deployment conditions. For temporal data, use time-based splits to prevent future infor
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yourquant_rick on Instagram: "Chapter 6 of Python for Algorithmic Trading marks the transition from handcrafted statistical analysis to machine learning (ML) models that can detect patterns in financial data automatically. It explains how supervised and unsupervised learning methods can be applied to trading problems, while stressing the dangers of overfitting noisy markets. Key topics and recipes include: Supervised learning basics: Linear regression, logistic regression, decision trees, random
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