In this session, we'll explore two critical concepts in machine learning: overfitting and underfitting. Understanding these concepts is essential for building models that generalize well to new data and make accurate predictions.
All our Data Science projects include bite-sized activities to test your knowledge and practice in an environment with constant feedback.
All our activities include solutions with explanations on how they work and why we chose them.
multiplechoice
True or False: Overfitting occurs when a model is too complex and fits the training data too closely, including noise. This can lead to poor generalization on new, unseen data.
multiplechoice
True or False: Validation curves help in determining the best hyperparameters for a model by plotting the training and testing error against different values of the hyperparameter.
Author
Verónica Barraza
This project is part of
Introduction to Supervised Learning with scikit-learn
