Interview Q&A / Machine Learning
Machine Learning interview questions
Supervised vs unsupervised learning, overfitting, metrics and the classic models, explained simply.
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- What do the C and gamma parameters do in an RBF SVM?
C controls how much misclassification is tolerated: high C fits training data tightly (risk overfitting), low C allows a wider, smoother margin. Gamma sets how far one point's influence reaches: high gamma gives wiggly, local boundaries, low gamma gives smooth ones.
IntermediateQuick answer1 min read - What is the kernel trick in SVM?
A kernel computes the dot product of two points as if they were mapped to a higher-dimensional space, without actually building that space. It lets a linear SVM draw curved boundaries cheaply. Common kernels: linear, polynomial, RBF.
IntermediateQuick answer1 min read