Overfitting §

Group: 4 #group-4

Relations §

• Backpropagation: Backpropagation can lead to overfitting if not properly regularized or if the training data is not representative.
• Underfitting: Underfitting and overfitting are two extremes of model complexity. While underfitting results from oversimplified models, overfitting occurs when models are too complex and overfit the training data.
• Decision Trees: Decision Trees can overfit the training data if not properly pruned
• Deep Learning: Overfitting is a common challenge in deep learning, where models perform well on training data but poorly on new data.
• Neural Networks: Overfitting is a common issue in neural networks, where the model performs well on the training data but fails to generalize to new, unseen data.
• Bias-Variance Tradeoff: Overfitting occurs when the model is too complex and fits the training data too closely, including noise, resulting in high variance and poor generalization to new data.
• Regularization: Regularization helps prevent overfitting by adding a penalty term to the loss function, which limits the complexity of the model.
• Convolutional Neural Networks: Overfitting is a common issue in Convolutional Neural Networks that needs to be addressed
• Machine Learning: Overfitting occurs when a Machine Learning model performs well on the training data but fails to generalize to new, unseen data.