less than 1 minute read


The Problem of Overfitting : article

Linear Regression/Logistic Regression:

弱弱 剛好 吃太補
underfit just right overfit
High bias just right high variance


Overfitting: If we have too many features, the learned hypothesis may fit the training ser very well, but fail to generalize to new examples.

Addressing overfitting:

  1. Reduce number of features
    • Manually select which features to keep.
    • Model selection algorithm.
  2. Regularization
    • Keep all the features, but reduce magnitude/ value of parameters θj.
    • Works well when we have a lot of features, each of which contributes a bit to predicting y.