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To understand a little bit more about overfitting and underfitting, I found this example:

Let鈥檚 Imagine we have three students, let鈥檚 call them student A, B and C respectively

  • Student A is not interested in learning and he/she doesn鈥檛 pay much attention
  • Student B pays attetion in class, but he/she memorizes absolutely all that professor says, not learning the main concepts
  • Student C pays attention in class and he/she learns the gist concepts from the classes, not memorizing but learning

Now, the professor decides to take a class test previosly before the official test, the class test would be the training set and the official test would be the test set


  • Student A obtains 50% in this class test because he/she is not interested in learning and hence in the official test she/he will obtain a bad result as well (Underfitting)
  • Student B obtains 98% in this class test because he has memorized all, but he will obtain a lower score in the official test because he/she will have difficulties to face the new information that he/she didn鈥檛 see in classes and the class test, since he/she doesn鈥檛 learn just memorize (Overfitting)
  • Student C obtains quite similiar results in both cases, because this student learned the main concepts and doesn鈥檛 have problems with new information (Ideal Case)


Reference link: https://www.analyticsvidhya.com/blog/2020/02/underfitting-overfitting-best-fitting-machine-learning/

Muy buen curso