regularisationnissa

Regularisationnissa, also known as regularisation, is a technique used in machine learning to prevent overfitting and improve the generalisation of models. Overfitting occurs when a model learns the training data too well, including its noise and outliers, which can lead to poor performance on new, unseen data. Regularisation addresses this issue by adding a penalty to the loss function, discouraging the model from fitting the training data too closely.

There are several types of regularisation techniques, including L1 and L2 regularisation. L1 regularisation, also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds a penalty equal to the absolute value of the magnitude of coefficients. This can lead to sparse models, where some coefficients are exactly zero, effectively performing feature selection. L2 regularisation, also known as Ridge, adds a penalty equal to the squared magnitude of coefficients, which tends to distribute error among all features.

Another form of regularisation is dropout, commonly used in neural networks. Dropout randomly sets a fraction of input units to zero at each update during training time, which helps prevent units from co-adapting too much. This technique can be seen as a form of model averaging, where many different "thinned" networks are trained simultaneously.

Regularisation is crucial in machine learning as it helps in building models that generalise well to new data. By preventing overfitting, regularisation ensures that the model's performance is consistent across different datasets, making it more reliable and robust.