When a statistical model or machine learning method catches the noise in the data, it is called overfitting. As a result, an algorithm’s outcome has a low bias but a big variance.
The following techniques can be used to avoid overfitting:
Cross-validation: The concept behind cross-validation is to divide the training data into several smaller train-test divisions. After that, you may utilize the splits to fine-tune your model.
More training data: More data can aid in improved analysis and categorization when fed into the machine learning model. This, however, does not always work.
Remove features: Frequently, the data collection contains characteristics or predictor factors that are unrelated to the investigation. Such characteristics merely add to the model’s complexity, perhaps resulting in data overfitting. As a result, unnecessary variables must be eliminated.
Early stopping: A machine learning model is iteratively trained, allowing us to assess how well each iteration works. The model’s performance begins to saturate after a certain number of iterations. Further training will result in overfitting, therefore it’s important to know when to quit. This is accomplished by a technique known as early halting.
Regularization: Regularization may be accomplished in a variety of methods, depending on the type of learner you’re using. Pruning is utilized on decision trees, the dropout technique is used on neural networks, and parameter tweaking can be used to alleviate overfitting problems.
Use Ensemble models: Ensemble learning is a method for creating numerous Machine Learning models that are then merged to provide more precise results. This is one of the most effective methods for avoiding overfitting. Random Forest is an example of how an ensemble of decision trees may be used to produce more accurate forecasts and minimize overfitting.