Regularization is a technique used in machine learning to prevent overfitting, which happens when a model learns the training data too well and performs poorly on new, unseen data.
Think of it like a balance in playing a sport. If you only focus on strengthening one particular skill, you might get really good at it but neglect other important skills. To be a well-rounded player, you need a balance of skills. Regularization is a bit like that balance.
In machine learning, a model is trained on a set of data called the training set. The goal is to find the model parameters that minimize the loss function, which measures the difference between the model’s predictions and the actual values. However, if the model fits the training data too well, it might not generalize well to new data. This is where regularization comes in.
Regularization adds a penalty to the loss function for complex models. This penalty discourages the model from relying too much on any single feature and encourages it to spread out its attention over multiple features. By doing this, regularization helps the model to be more balanced and to generalize better to new data.
There are different types of regularization, like L1 and L2 regularization, which add different types of penalties to the loss function.
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