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
Admin Staff asked 4 years ago
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