In Kernel Ridge Regression (KRR), the parameters alpha and gamma serve specific purposes:
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Alpha:
- Regularization Parameter: Alpha controls the amount of regularization applied to the model. A higher alpha value increases the penalty for large coefficients, which helps prevent overfitting by discouraging overly complex models. Conversely, a lower alpha allows the model to fit the training data more closely, which can lead to overfitting.
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Gamma:
- Kernel Coefficient: Gamma is a parameter specific to certain kernel functions, such as the Radial Basis Function (RBF) kernel. It determines the influence of a single training example. A small gamma value means that the influence of each training point is spread out, leading to a smoother decision boundary. A large gamma value makes the influence more localized, which can lead to a more complex model that fits the training data closely.
Together, these parameters help balance the trade-off between bias and variance in the model, affecting its performance on unseen data.
