What are the different types of loss functions in machine learning?
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What are the different types of loss functions in machine learning?
In machine learning, loss functions measure how well a model's predictions match the actual outcomes. Common types include Mean Squared Error (MSE), Cross-Entropy Loss, and Hinge Loss.
Can you explain Mean Squared Error?
Mean Squared Error (MSE) calculates the average of the squares of the errors, which are the differences between predicted and actual values. It's commonly used in regression tasks.
What about Cross-Entropy Loss?
Cross-Entropy Loss is used primarily in classification tasks. It measures the difference between two probability distributions - the true distribution and the predicted distribution.
Are there any other loss functions I should know about?
Yes, there are several others like Hinge Loss for SVMs, Kullback-Leibler Divergence for comparing probability distributions, and Focal Loss for addressing class imbalance.
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