regularisaatiosta
Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data. Overfitting is particularly common in complex models with many parameters, such as deep neural networks. Regularization introduces additional information or constraints to the learning algorithm, encouraging it to produce simpler models that generalize better to new data.
There are several common regularization techniques:
1. L1 Regularization (Lasso): Adds a penalty equal to the absolute value of the magnitude of coefficients
2. L2 Regularization (Ridge): Adds a penalty equal to the squared magnitude of the coefficients to the
3. Elastic Net: Combines L1 and L2 regularization, providing a balance between the two. It is useful
4. Dropout: Used primarily in neural networks, dropout randomly sets a fraction of input units to zero
5. Early Stopping: Monitors the model's performance on a validation set during training and stops the training
Regularization is crucial for building robust machine learning models that perform well on unseen data. By