A Loss functions guides model training by providing a measure of prediction accuracy. The ultimate aim is to minimize the loss, ensuring the model makes accurate predictions on unseen data.
Terminology
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Loss Function: Also referred to as a cost function, error function, or objective function. It is a component in training machine learning models.
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Reward Function: Mentioned as the opposite of a cost function, typically used in reinforcement learning to indicate the desirability of an outcome.
Description
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Purpose: A loss function quantifies how well a model’s predictions align with the actual target values. It serves as a guide for the optimization process during model training.
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Minimization: The primary goal is to minimize the loss function, which indicates that the model’s predictions are becoming more accurate.
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Overfitting: The note hints at the relationship between loss functions and overfitting. A model that fits the training data too well (low training loss) might not generalize to new data, leading to overfitting.
Examples
- Types of Problems: The note suggests that different types of problems (e.g., regression, classification) use different loss functions. For instance, cross entropy is commonly used for classification tasks.
Evaluation
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Disparity Measurement: The loss function measures the difference between predicted and actual values, providing a metric for model evaluation.
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Training and Evaluation: Loss functions are used both during training (to adjust model parameters) and evaluation (to assess model performance).