Mean Squared Error
Mean squared error (MSE) is a loss function that measures the average of the squared differences between predicted and actual values, commonly used to train and evaluate regression models.
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Glossary Terms(4)
L1 Regularization
L1 regularization is a technique that adds a penalty proportional to the sum of the absolute values of a model's weights to its loss function, encouraging spar…
Stochastic Gradient Descent
Stochastic gradient descent (SGD) is an iterative optimization algorithm that updates model parameters using the gradient of the loss computed on a small, rand…
Cross-Entropy Loss
Cross-entropy loss is a loss function that measures the difference between a predicted probability distribution and the true (target) distribution, commonly us…
Mean Squared Error
Mean squared error (MSE) is a loss function that measures the average of the squared differences between predicted and actual values, commonly used to train an…