machine learning

Question 2010 pointa) Gene data is usually very large and hard to apply classification en it before reducing it. We can either apply feature reduction on the gene data or gene selection Give one example where it is better ta do feature reduction than feature selection Question 3 [10 points) Name only one classifier that has the following hyper-plane. Use different answer and do not repeat the same anaver. Explain why 1 - One Linear hyper plan 2- The hyperplane is a sphere. 3- a set of piecewise linear hyperplanes - The purplane plan in the middle of the two classes Question 2 [10 points] Gene data is usually very large and hard to apply classification on it before reducing it. We can either apply feature reduction on the gene data or gene selection Give one example where it is better to do feature reduction than feature selection Question 2010 pointa) Gene data is usually very large and hard to apply classification en it before reducing it. We can either apply feature reduction on the gene data or gene selection Give one example where it is better ta do feature reduction than feature selection Question 3 [10 points) Name only one classifier that has the following hyper-plane. Use different answer and do not repeat the same anaver. Explain why 1 - One Linear hyper plan 2- The hyperplane is a sphere. 3- a set of piecewise linear hyperplanes - The purplane plan in the middle of the two classes Question 2 [10 points] Gene data is usually very large and hard to apply classification on it before reducing it. We can either apply feature reduction on the gene data or gene selection Give one example where it is better to do feature reduction than feature selection