L2förlust

L2förlust, also known as L2 loss or squared loss, is a commonly used loss function in machine learning, particularly in regression tasks. It measures the average of the squares of the errors—the differences between the predicted values and the actual values. The formula for L2 loss is:

L2 loss = (1/n) * Σ(y_i - ŷ_i)^2

where n is the number of data points, y_i is the actual value, and ŷ_i is the predicted value. The L2 loss function is differentiable and convex, making it suitable for optimization using gradient descent and its variants.

One of the key properties of L2 loss is that it penalizes larger errors more than smaller ones due to the squaring operation. This characteristic can be beneficial in scenarios where larger errors are more critical, but it can also lead to overfitting if not managed properly.

L2 loss is often used in conjunction with regularization techniques, such as L2 regularization (also known as ridge regression), to prevent overfitting. Regularization adds a penalty term to the loss function, proportional to the square of the magnitude of the coefficients, encouraging the model to keep the coefficients small.

In summary, L2förlust is a fundamental loss function in machine learning, widely used in regression tasks due to its simplicity and effectiveness. Its properties make it a valuable tool in the development of robust and accurate predictive models.