What does "Generalisation" mean?
Table of Contents
Generalisation refers to a model's ability to perform well on new, unseen data after it has been trained on a specific dataset. In simple terms, it's about how well a machine learning system can apply what it learned from one set of examples to different situations or data it hasn't seen before.
Importance of Generalisation
Good generalisation is crucial for making sure that a model works effectively in the real world. For instance, if a model is trained to recognize certain types of images, its ability to identify similar but different images is what shows how well it can generalise. If it only performs well on the images it was trained on, it might not be very useful in practice.
Factors Affecting Generalisation
Several factors can impact a model's ability to generalise. These include:
- Training Data: The type and variety of data used during training. More diverse data can help improve generalisation.
- Model Architecture: Different types of models can behave differently. Some may generalise better than others based on their design.
- Pretraining: Using models that have been trained on related data can give them a head start, allowing them to generalise better when faced with new data.
Real-World Application
In healthcare, for instance, a model trained to identify polyps in one set of images may face challenges when using images from another hospital or different patient demographics. The model needs to generalise well to accurately identify polyps in these new images.
Overall, generalisation is a key concept in machine learning that determines how well models can adapt and function in new environments or with different data.