5,321
edits
Machine Learning: Difference between revisions
→Bias-Variance Tradeoff
| Line 176: | Line 176: | ||
: and <math>L_S(h)</math> be the true loss of hypothesis h | : and <math>L_S(h)</math> be the true loss of hypothesis h | ||
* <math>L_D(h_s^*) = L_D(h_D^*) + [L_D(h_s^*) - L_D(h_D^*)]</math> | * <math>L_D(h_s^*) = L_D(h_D^*) + [L_D(h_s^*) - L_D(h_D^*)]</math> | ||
* The term <math>L_D(h_D^*)</math> | * The term <math>L_D(h_D^*)</math> is called the bias | ||
* The term <math>[L_D(h_s^*) - L_D(h_D^*)]</math> is called variance. | * The term <math>[L_D(h_s^*) - L_D(h_D^*)]</math> is called variance. | ||
* Larger hypothesis class will get smaller bias but larger variance. | * Larger hypothesis class will get smaller bias but larger variance. | ||
* Overfitting vs. underfitting | * Overfitting vs. underfitting | ||