Overfitting Intuition
Explore the concept of overfitting in supervised learning and decision trees. Understand why a model can perform well on training data but poorly on new data, and learn to recognize when models become too specialized. This lesson lays the groundwork for avoiding overfitting to create more reliable machine learning models.
We'll cover the following...
The persistent challenge in machine learning
A critical concept in using machine learning effectively is overfitting. Overfitting is where a model’s predictions are much less accurate for data not used in training the model (i.e., less accurate with new data).
Now here’s the thing.
Crafting machine learning models that overfit is ridiculously easy. Much of what machine learning practitioners do to craft the most valuable models (e.g., engineering new features) tends to make the models overfit. ...