Loss functions are used in training machine learning models. Also known as a cost function, error function, or objective function. Serves as a metric for model evaluation.
Purpose: Measure predictive accuracy: Measures the difference between predicted and actual values. That is they measure how well a model’s predictions match the actual target values by quantifying the error between the predicted output and the true output.
Goal: To be minimized: The primary goal during model training is to minimize this loss, improving accuracy of predictions on unseen data.
Used during training to adjust model parameters and during evaluation to assess model performance.
Examples
- Mean Squared Error (MSE): Commonly used for regression tasks.
- Cross Entropy: Often used for classification tasks.
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