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
Another form of regularisation is dropout, commonly used in neural networks. Dropout randomly sets a fraction
Regularisation is crucial in machine learning as it helps in building models that generalise well to new