Question

High-dimensional spaces arise as a way of modelling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space with its position depending on its attribute values. Which of the following algorithms is best when it comes to clear margin of separation & high dimensional spaces ?
O Support Vector Machine
O Linear regression
O Random forest
O Logistic Regression​

Answer 1

Answer:

Support Vector Machine

Explanation:

SVM light is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following: fast optimization algorithm working set selection based on steepest feasible descent" shrinking" heuristic caching of kernel evaluations use of folding in …

Answer 2

Answer:

High dimensional spaces arise as a way of modeling datasets with many attributes. Such a dataset can be directly represented in a space spanned by its attributes, with each record represented as a point in the space, with its position depending on its attribute values. Which of the following algorithms is best when it comes to a clear margin of separation & high dimensional spaces?

a.Random forest

b.Logistic Regression

c.Linear regression

d.Support Vector Machine