Benign Overfitting and Grokking in ReLU Networks for XOR Cluster

Overfitting can lead to misleading results and poor decision-making, while underfitting can result in models that fail to capture important patterns and

Overfitting describes when a model becomes too sensitive to noise in its training set, leading it to not generalize, or to generalize poorly, to new data

overfitting Overfitting is a common problem in machine learning when a model is trained to fit the training data too closely and therefore, it performs

overfitting Overfitting is caused by the size of the data We know that the more the data more the model will learn hence, we try to give better predictions