Overfitting, in machine learning, occurs when a model becomes too focused on the specific training data it’s been trained on, at the expense of its ability to generalize to new, unseen data. Imagine a student studying for an exam by memorizing every single practice question and answer, but then completely bombing the actual exam because the questions were phrased differently.
As a mental model, focusing on detail and memorization is more harmful to your end goal that understanding the broad concept being studied.
Overfitting as Memorization vs. Understanding:
- Overfit (Memorize): Focus on memorizing every detail from your textbook and practice problems. You might even memorize the question format and specific wording. This might help you ace practice tests, but if the actual exam questions are phrased differently or ask for slightly different applications of knowledge, you’ll struggle.
- Good Fit (Understanding): Focus on understanding the core concepts and principles behind the material. You can then apply this knowledge to solve new problems even if they are presented in a different way.
Avoiding Overfitting in Real Life:
- Job Training: Focusing too narrowly on the specific tasks you perform in your current role might limit your adaptability when seeking new opportunities. Aim for a broader understanding of the industry and transferable skills.
- Problem-Solving: Always resorting to the exact solution that worked in a past situation can hinder creativity and limit your ability to tackle new problems that require a different approach. Focus on understanding the underlying principles and be open to adapting your solutions.
Overfitting highlights the importance of balancing specific knowledge with a broader understanding of core principles. This allows you to adapt, generalize, and perform well in new situations, just like a well-trained machine learning model should.