Feature selection: When you build a model, you’ll have a number of parameters or features that are used to predict a given outcome, but many times, these features can be redundant to others.
However, this method should be done sparingly. Data augmentation: While it is better to inject clean, relevant data into your training data, sometimes noisy data is added to make a model more stable.Otherwise, you could just continue to add more complexity to the model, causing it to overfit. That said, this is a more effective method when clean, relevant data is injected into the model. Train with more data: Expanding the training set to include more data can increase the accuracy of the model by providing more opportunities to parse out the dominant relationship among the input and output variables.Finding the “sweet spot” between underfitting and overfitting is the ultimate goal here. This approach risks halting the training process too soon, leading to the opposite problem of underfitting. Early stopping: As we mentioned earlier, this method seeks to pause training before the model starts learning the noise within the model.Below are a number of techniques that you can use to prevent overfitting:
In addition to understanding how to detect overfitting, it is important to understand how to avoid overfitting altogether. While using a linear model helps us avoid overfitting, many real-world problems are nonlinear ones. Underfitting occurs when the model has not trained for enough time or the input variables are not significant enough to determine a meaningful relationship between the input and output variables. However, if you pause too early or exclude too many important features, you may encounter the opposite problem, and instead, you may underfit your model. If overtraining or model complexity results in overfitting, then a logical prevention response would be either to pause training process earlier, also known as, “early stopping” or to reduce complexity in the model by eliminating less relevant inputs. If the training data has a low error rate and the test data has a high error rate, it signals overfitting.
In order to prevent this type of behavior, part of the training dataset is typically set aside as the “test set” to check for overfitting. Low error rates and a high variance are good indicators of overfitting. If a model cannot generalize well to new data, then it will not be able to perform the classification or prediction tasks that it was intended for. When the model memorizes the noise and fits too closely to the training set, the model becomes “overfitted,” and it is unable to generalize well to new data. However, when the model trains for too long on sample data or when the model is too complex, it can start to learn the “noise,” or irrelevant information, within the dataset. When machine learning algorithms are constructed, they leverage a sample dataset to train the model. Generalization of a model to new data is ultimately what allows us to use machine learning algorithms every day to make predictions and classify data. When this happens, the algorithm unfortunately cannot perform accurately against unseen data, defeating its purpose. Overfitting is a concept in data science, which occurs when a statistical model fits exactly against its training data. Learn how to avoid overfitting, so that you can generalize data outside of your model accurately.