[ML Tutorials #2] "Understanding Overfitting and Underfitting in a Quick 90-Second Read" 2778
Overfitting and underfitting represent two common issues in machine learning that affect the performance of a model. In the context of overfitting, the model learns the training data too precisely, capturing noise and fluctuations that are specific to the training set but do not generalize well to new, unseen data. Underfitting, on the other hand, occurs when a model is enabled to capture the underlying patterns in the training data, resulting in poor performance not only on the training set but also on new, unseen data. It indicates a failure to learn the complexities of the data.
**Analogy : **
Intuitively, returning to the example of the student that we presented in the definition of the machine learning concept, we discussed the possibility of considering a machine learning model as a student in a class. After the lecture phase, equivalent to the training step for the model, the student takes an exam or quiz to confirm their understanding of the course material. Now, imagine a student who failed to comprehend anything during the course and did not prepare. On the exam day, this student, having failed to grasp the content, will struggle to answer and will receive a low grade; this represents the case of underfitting in machine learning. On the other hand, let's consider another student who, despite having a limited understanding of the course, mechanically memorized the content and exercises. During the exam, when faced with questions reformulated or presented in a new manner, this student, having learned without true comprehension, will also fail due to the inability to adapt, illustrating the case of overfitting in machine learning.
This analogy between a machine learning model and a student highlights the insightful parallels of underfitting and overfitting. Just as a student can fail by not grasping the course or memorizing without true understanding, a model can suffer from underfitting if it's too simple to capture patterns or overfitting if it memorizes the training data too precisely. Striking the right balance between complexity and generalization is crucial for developing effective machine learning models adaptable to diverse and unknown data. In essence, this educational analogy emphasizes the delicate equilibrium required in the machine learning learning process.
