GMR Group Data Analyst Intern Interview Questions and Answers

Python programming for 45 for minutes

Q1. What are the hyperparameters of SVM
Ans. 
Hyperparameters of SVM include C, kernel, gamma, degree, and coef0.
• C: Regularization parameter that controls the trade-off between achieving a low error on the training data and minimizing model complexity.

• Kernel: Specifies the type of hyperplane used to separate the data.

• Gamma: Kernel coefficient for 'rbf', 'poly', and 'sigmoid' kernels.

• Degree: Degree of the polynomial kernel function.

• Coef0: Independent term in kernel...read more
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Q2. How to tune that hyperparameters
Ans. 
Hyperparameters can be tuned using techniques like grid search, random search, and Bayesian optimization.
• Grid search: Exhaustively search through a specified subset of hyperparameters.

• Random search: Randomly sample hyperparameter combinations.

• Bayesian optimization: Use probabilistic models to predict the performance of different hyperparameter configurations.
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