Kernel Methods in Machine Learning with Python

Kernel Methods in Machine Learning with Python
Kernel methods are a powerful class of machine learning algorithm that allow us to perform complex, non-linear transformations of data without explicitly computing the transformed feature space. These methods are particularly useful when dealing with high-dimensional data or when the relationship between features is non-linear.
Why would we use kernel methods?
Non-linearity: Kernel methods can capture non-linear relationships in data by mapping it to a higher-dimensional space where linear methods can be applied
Efficiency: By using the kernel trick, we avoid the computational cost of explicitly transforming the data
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