Overfitting is a common problem encountered by data scientists, especially when attempting to model complex relationships. This article will discuss the definition of overfitting, the underlying causes, and strategies to avoid it.
Overfitting is a statistical inference problem that occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. In this situation, the model "overlearns" the data and, as a result, has poor predictive performance on unseen or test data. Overfitting is one of the most common issues in machine learning and occurs when the model fits itself to specific noise or random errors in the training data. This means that the model does not generalize well to unseen data and when it is used to make predictions. This can lead to inaccurate predictions and a poor overall model performance.
Overfitting can occur when a model is too complex for the amount of data it is being trained on, and is essentially memorizing the data instead of fitting itself to a general pattern. This tends to happen when an overly complex model, such as a deep neural network, is used for smaller datasets. Additionally, overfitting can occur when there are too many features in the data, making it difficult for the model to draw meaningful correlations among them. Lastly, overfitting can be caused by data leakage, which happens when data from the testing set is accidentally used to tune the model, resulting in artificially high training scores.
To summarize, overfitting often occurs when a model is too complex for the data it is using, there are too many features in the data, or data leakage is present. Taking steps to prevent these causes, such as using simple models for small datasets, avoiding redundant features, and properly separating the training and testing data, can help avoid the issue of overfitting.
When it comes to strategies for avoiding Overfitting, there are a few options to consider. One of the most common is regularization, which is a process of adding a penalty to the loss function of a model in order to reduce the complexity of the model and prevent overfitting. Another option is to use cross validation techniques such as k-fold cross validation, which breaks the data into multiple subsets and trains and validates the model on each of them. This ensures that the model can better generalize to unseen data. Finally, it is important to ensure that the model has enough data to train on so that it can accurately learn the patterns without overfitting. Having more data will also allow for better regularization techniques to be employed.