How to Avoid Overfitting in Machine Learning Models
Machine learning models have become the hype of the technology industry. These models help businesses to solve complex problems, and provide intelligent solutions to achieve their goals. However, there is one issue that troubles players in the field of machine learning: overfitting. Overfitting in machine learning models leads to poor results and can even damage the reputation of a business. In this blog post, we will discuss what overfitting is, its effects, and how to avoid it.
Introduction
Machine learning models are built to recognize patterns and discover relationships among data. After training, a machine learning model should be able to predict future outcomes based on data fed into it. However, sometimes, the model is so good at recognizing the patterns in the training data that it begins to focus too much on the idiosyncrasies of the training data. This situation is called overfitting.
What is Overfitting?
Overfitting is a situation that occurs when a machine learning model is too complex that it starts to memorize the training data instead of learning from it. The model performs exceptionally well on the training data, but poorly on unseen data. Overfitted models detect patterns in the training data that are not unique to the data, which results in a model that is too specific to be useful in other scenarios.
Why is Overfitting a Problem?
Overfitting poses significant challenges to machine learning models. For one, the model does not generalize well, which means it has low accuracy when it comes to predicting outcomes with new data. Secondly, overfitted models cause high bias, which means that they underestimate the systematic relationship between variables. Overfitted models also lead to high variance, which simply means that the model has high randomness, leading to unstable predictions.
Strategies to Avoid Overfitting
There are several strategies that you can use to avoid overfitting. These strategies include:
Use more data
Using more data leads to a more generalized model. The model will be able to detect trends within the data better and recognize patterns that are common across different data sets. A generalized model should be more accurate and reliable than an overfitted model.
Use simpler models
Simple models tend to be less prone to overfitting. By reducing the complexity of your model, you reduce the number of parameters it has to learn, leading to less memorization of the training data.
Use regularization
Regularization is a method used to add a penalty on the model’s complexity. This penalty discourages the model from creating too many parameters leading to overfitting. Regularization helps to reduce overfitting by shrinking the parameter coefficients toward zero, resulting in simple models.
Conclusion
Overfitting is not an uncommon problem in machine learning. It often leads to poor predictions, which can damage the reputation of a business. To avoid overfitting, machine learning experts should aim to use more data, simpler models, and regularization. Following these strategies will lead to a better-performing model that is reliable and provides accurate predictions.
