Demystifying Loss Functions: A Guide to Key Principles for Evaluating Machine Learning Models
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Explains what loss functions are and why they are important for evaluating machine learning models during training.
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Discusses different commonly used loss functions like MSE, MAE, log loss, cross-entropy loss.
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Explains principles for designing loss functions based on the predictive task, data distribution, model type.
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Shows connections between log loss and cross-entropy loss.
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Compares MSE and RMSE loss functions.