Overview
Overfitting and underfitting are critical concepts in machine learning that affect a model's ability to generalize to new data. Overfitting occurs when a model learns the training data too well, capturing noise rather than the underlying patterns, leading to poor performance on unseen data. Converse...
Key Terms
Example: A model that predicts training data perfectly but fails on new data.
Example: A linear model trying to fit a complex, non-linear dataset.
Example: A deep neural network has high complexity compared to a linear regression model.
Example: A model that consistently misses the target due to its simplicity.
Example: A model that performs well on training data but poorly on unseen data.
Example: Lasso and Ridge regression are common regularization techniques.