refers to a model that models the training data too well. In other words, the model is still learning patterns but they do not generalize beyond the training set。
Overfitting happens 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 that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.
Overfitting is particularly typical for models that have a large number of parameters, like deep neural networks.
- simpler model structure（选择合适模型）
- data augmentation（数据集扩增）
- early stopping（提前终止迭代）
- utilize invariance（利用不变性）
refers to a model that can neither model the training data nor generalize to new data.
An underfit machine learning model is not a suitable model and will be obvious as it will have poor performance on the training data.
欠拟合（Underfitting）& 过拟合（Overfitting） - 知乎
机器学习防止欠拟合、过拟合方法 - 知乎