Machine Learning 1: UNIT-5(A) NOTES: Dimensionality Reduction NOTES
Unit V
Dimensionality Reduction
1. The
Curse of Dimensionality
2. Main
Approaches for Dimensionality Reduction
3. Projection
4. PCA
Machine Learning 1: UNIT 3 : Support Vector Machines MCQs
UNIT 3 Support Vector Machines MCQs ----------------------------------------------------------------------------------------------------------------------------- 1. A Support Vector Machine can be used for A. Performing linear or nonlinear classification B. Performing regression C. For outlier detection D. All of the above Ans: D 2. The decision boundaries in a Support Vector machine is fully determined (or “supported”) by the instances located on the edge of the street? Top of Form True False Ans: A 3. Support Vector Machines are not sensitive to feature scaling A. Top of Form True False Ans: B 4. If we strictly impose that all instances be off the street and on the right side, this is called Soft margin classification Hard margin classification Strict margin classification Loose margin classification Ans...
Comments
Post a Comment