SVM HYPERPARAMETERS

An educational illustration representing Support Vector Machines (SVM) and their hyperparameters, showing elements like graphs, kernels, and data classification in a visually engaging format.

SVM Hyperparameters Mastery Quiz

Test your knowledge of Support Vector Machines (SVM) hyperparameters with this engaging quiz! Whether you are a beginner or looking to sharpen your skills, this quiz will challenge your understanding of key concepts in machine learning.

10 enlightening questions
Multiple choice and open-ended formats
Perfect for students and professionals alike

10 Questions2 MinutesCreated by OptimizingSVM42

What hyperparameters refers to

Model pararmeter that we can customize or adjust

Model pararmeter that we can'tcustomize or adjust

What happend if the regularization parameter C is large

Misclassification- would happen

What is the best practice to do for a best model result

Keep the longest c value

Keep the large c value

Look at the cross-validation score to have effective combination of these parameters and avoid over-fitting.

Why we use train_test_split function in sckit learn

To split the data into test and train

To only train the model

With each kernel the gamma hyperparameter

All the kernels

With the rbf kernel

Whith the linear kernal

Suppose you are dealing with 4 class classification problem and you want to train a SVM model on the data for that you are using One-vs-all method.

What binary classification refers to

He machine should classify an instance as only one of two classes; yes/no, 1/0, or true/false.

It's a classification that tacles multiclasses classification

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SVM HYPERPARAMETERS

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