In SVM which has quadratic kernel function of polynomial degree 2 that has slack variable C as one hyper paramenter. What would happen if we use very large value for C
A. we can still classify the data correctly for given setting of hyper parameter c
B. we can not classify the data correctly for given setting of hyper parameter c
C. we can not classify the data at all
D. data can be classified correctly without any impact of c
Answer: Option A
This Question Belongs to Computer Science >> Machine Learning
In simple term, machine learning is
A. training based on historical data
B. prediction to answer a query
C. both A and B
D. automization of complex tasks
Which of the following is the best machine learning method?
A. scalable
B. accuracy
C. fast
D. all of the above
The output of training process in machine learning is
A. machine learning model
B. machine learning algorithm
C. null
D. accuracy
Application of machine learning methods to large databases is called
A. data mining.
B. artificial intelligence
C. big data computing
D. internet of things
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