Overfitting and regularization in Machine Learning #ai #machinelearning #overfitting #regularization

27 May 2025 · 2 min

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The speaker explains that the quality and quantity of training data are crucial for building high‑performance machine‑learning models. Too little data prevents the model from capturing underlying patterns, while excessive data can lead to overfitting, where the model memorizes noise and loses the ability to generalize. Overfitting is identified by high training metrics but significantly lower performance on test data, especially when test data differ slightly in distribution. To mitigate overfitting, the speaker recommends training on a balanced, diverse dataset and applying regularization techniques specific to neural networks: L1 (LASSO) and L2 (Ridge) weight decay, dropout, batch normalization, and early stopping that monitors validation performance and halts training when overfitting signs appear.

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We know that a machine learning model learns from data. So, high-quality data contributes significantly to the development of a high-performance model with highly accurate predictive abilities. However, the training data must be just right. Indeed, if the model is not trained with enough data, then it may not learn all the subtleties and subjacent trends effectively. As a result, its predictions in the real world won't be satisfying either. And if, on the opposite, it's trained on far too much data, then, in addition to what this requires in terms of processing time and computing power, it may fall into the overfitting problem. Overfitting is a well-known problem in machine learning. It occurs when the model learns too many specific details of the data and even the noise present in it, leading it to lose all sense of generalization on new examples. Overfitting is generally recognized by high performance metrics on training data, whereas these metrics are lower on test data, even more so if the data have slightly different trends, in which case the model becomes almost ineffective. To prevent overfitting, we can start by training the model on a balanced amount of data with the required diversity. We can also apply regularization techniques such as weight decay using the L1 and L2 variants, also known as LASSO and Ridge, dropout, or batch normalization, which are regularization techniques specific to neural networks. Another technique technique is early stopping, which monitors performance on validation data and stops learning when signs of overfitting start to appear.