Overfitting is a common challenge in machine learning. This refers to the issue of a model performing exceptionally well on the training data while performing poorly on unseen data. This issue arises when the model captures noise or irrelevant patterns in the training data, leading to poor generalisation. Regularisation techniques like L1 and L2 are powerful tools to address overfitting by penalising overly complex models. This article delves into L1 and L2 regularisation, their differences, and their implementation in preventing overfitting, a topic often covered in a comprehensive Data Science Course that has coverage on machine learning.

Understanding Regularisation
Regularisation adds a term of penalty to the loss function during model training, discouraging overly large coefficients in the model’s parameters. This encourages simpler models that are less prone to overfitting while maintaining predictive power. If you have taken an advanced-level Data Science Course in pune and such learning hubs, you will already know that regularisation is essential for improving model generalisation.

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