Generalization refers to an ML model’s capability of accurately performing on new and unknown data points previously not used during the training phase. Essentially, a machine learning model with an effective generalization would be able to correctly categorize and predict results based on data it has not seen before. This is an important feature for these models to be considered for real-world applications.
ML models can generalize more effectively when they learn the basic patterns and relationships in the data rather than memorizing trained examples. However, this generalization ability is hindered when overfitting occurs. Overfitting refers to the model being too complex to fit the training data’s noise. Thus, models with good generalization are considered more robust and reliable to perform with real-world data.
How Does Generalization Make a Model Effective?
1. Robustness
Machine Learning models that generalize well on new data are considered more reliable and can perform with accuracy on unknown data.
2. Predictive accuracy
Models with effective generalization tend to make more accurate predictions based on real-world data, which makes them useful for speech recognition, natural language processing, and image recognition.
3. Data efficiency
Models that are effectively generalized deliver high accuracy with fewer data making them cost-effective and data-efficient.
4. Transfer learning
With generalization, models can transfer knowledge from one task to another, making them more efficient and faster to train new models.
5. Regularization
Regularization prevents overfitting and enhances generalization to enable models to learn the fundamental relationships and patterns in the data.
6. Explainability
Generalization enhances the models’ transparency and makes it easier to interpret and understand their prediction and decision-making abilities.
