Machine learning employs various methods to ensure a deep understanding rather than just rote memorization. When a model performs exceptionally well on the training data but fails to replicate this performance on test data, it's an indication that the model is merely memorizing the answers, not truly understanding them.
To illustrate, consider a model designed to differentiate between cats and dogs. If it achieves a 98% accuracy on the training set but only manages an 82% accuracy on test data, it suggests the model is memorizing patterns rather than genuinely comprehending them.
Real-World Scenario: E-commerce and User Behavior
Imagine an e-commerce platform wanting to predict buying behaviors based on user activity over the past week to improve digital ad retargeting. Using metrics like page visits, duration, search frequency, and others, they develop a model. This model might perform perfectly on known data but falters when exposed to new, unfamiliar data. Again, the model seems to be memorizing patterns rather than truly understanding user behavior.
One of the primary culprits of such behavior is overfitting. Overfitting occurs when a model is too closely tailored to a specific dataset, making it less effective when exposed to new data. If your model's evaluation metrics significantly differ between training and test datasets, overfitting is likely the issue.
Regularization techniques are designed to encourage genuine learning by moderating specific attributes in traditional algorithms or the neurons in neural networks. By adjusting the weights associated with a feature or neuron, regularization ensures that the model doesn't overly rely on specific features or patterns, helping prevent overfitting, simplifying models, and promoting effective feature selection.
Ridge Regression is a regularization technique that addresses challenges like overfitting and multicollinearity, especially when independent variables are closely related. By introducing a small squared bias factor, Ridge Regression adds a minor bias to the model, reducing its variance, making it a potent tool against overfitting.
LASSO (Least Absolute Shrinkage and Selection Operator) takes a stricter approach than Ridge Regression by heavily penalizing large coefficients. With a significant hyperparameter, LASSO can force some coefficient estimates to become zero, simplifying the model, especially when numerous features are involved.
A combination of Ridge Regression and LASSO, Elastic-NET works by both shrinking and selecting sparse features. It's particularly effective when dealing with a group of highly correlated predictors, choosing one while reducing others to zero.