Loss Functions Explained
In the journey of training a machine learning model, one of the most important components is the loss function. A loss function, also known as a cost function, measures how well your model is performing.
Loss functions are used to map the outputs of a machine learning model into a real number which represents the 'cost' associated with that model's prediction. The goal during training is to make this number as small as possible - essentially minimizing the loss.
Types of Loss Functions
- Mean Squared Error (MSE): A common choice for regression problems, MSE calculates the average squared difference between observed and predicted values.
- Cross-Entropy Loss: Often used in classification, cross-entropy measures the divergence between the predicted probability distribution and the true distribution.
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