Overfitting is a common problem in machine learning and statistics, where a model learns not only the underlying pattern in the training data but also the noise. This typically occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. Overfitting results in a model that performs well on the training data but poorly on new, unseen data. This is because the model's complexity has led it to learn details that do not generalize to other data sets, including random fluctuations in the training data.
One of the primary indicators of overfitting is a significant discrepancy between the performance metrics of the model on training data versus validation or test data. For example, a highly overfitted model might exhibit near-perfect accuracy on the training dataset but perform much worse on a new dataset. This happens because the model's complexity, which allowed it to perfectly map all the idiosyncrasies of the training data, does not adapt well to any deviations presented by new data. In technical terms, while the bias in the model is low, the variance is high, making the model sensitive to high variance in new data inputs.
To combat overfitting, data scientists employ various techniques. One common method is cross-validation, which involves dividing the dataset into several subsets and using each in turn for training and validation. Another technique is regularization, which adds a penalty term to the loss function used to train the model. This penalty term can discourage overly complex models by increasing the cost of having large weights, thus promoting simpler models. Techniques like dropout in neural networks—where randomly selected neurons are ignored during training—can also help reduce overfitting by making the neural network less sensitive to the specific weights of neurons.
Moreover, choosing the right model complexity is crucial in avoiding overfitting. This involves selecting the appropriate number of parameters or deciding the depth and width of a neural network. Simplifying the model might increase the bias slightly but significantly reduces the variance, leading to better generalization on new datasets. Additionally, increasing the size of the training data can help, as more data provides a better approximation of the real-world distribution and helps in building a robust model. Ultimately, the goal is to achieve a balance between bias and variance, enabling the model to generalize well without capturing unnecessary noise—a principle known as the bias-variance_tradeoff.