What is Model Overfitting?
Overfitting happens when a machine learning model gets trained too well on the so-called training data. While it may sound like a good thing, in reality, an overfitted model performs poorly when it encounters new, unseen data since it’s considered too tuned to the training set. A good model should generalize well, not just perform well with the training data, but with new data as well.
The concept of model overfitting is closely tied to model complexity. When a model is too complex, it has the capacity to fit the training data very closely, capturing even the smallest variations and fluctuations. While this may result in high accuracy on the training set, the model becomes excessively tailored to the specific characteristics of that data.
As a consequence, the model struggles to generalize to diverse data points outside the training set, exhibiting poor performance on new data. In essence, overfitting is often a consequence of excessive model complexity, highlighting the importance of finding the right balance between simplicity and complexity to ensure robust generalization.
Model Complexity & Overfitting in Machine Learning
Model complexity leads to overfitting, which makes it harder to perform well on the unseen new data. In this article, we delve into the crucial challenges of model complexity and overfitting in machine learning.
Table of Content
- What is Model Complexity?
- Why Model Complexity is Important?
- What is Model Overfitting?
- How to Avoid Model Complexity and Overfitting?