When Does Overfitting occur?

Overfitting occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is more pronounced in SVMs when the decision boundary is overly complex and tries to accommodate all training points.

To avoid over-fitting in SVMs, it’s crucial to find the right balance between the model’s ability to generalize well to new data and its capacity to fit the data it has been trained on. This involves understanding and carefully controlling the model’s complexity through its hyperparameters. The goal is to construct a model that is complex enough to capture the underlying patterns of the data, but not so complex that it doesn’t perform well on data outside of the training set.

avoid overfittingSupport Vector Machine (SVM) is a powerful, supervised machine learning algorithm used for both classification and regression challenges. However, like any model, it can suffer from over-fitting, where the model performs well on training data but poorly on unseen data.