Remember:
- factor (makes derivative cleaner)
- Sum over all data points
- Squared difference between:
- model prediction
- target/actual value
Mnemonic: “Half the Sum of Squared differences” Note: Sometimes written as for averaging
Generalized Squared Error
- is the parameter vector (weights) that the model learns (and optimized during training).
- is the Basis Function vector which Transforms the raw input x into a higher-dimensional feature space
- Transforms the raw input into a higher-dimensional feature space
- The dot product gives the model’s prediction