When dealing with high-dimensional data (p > n), techniques like regular

You are given a dataset where the number of variables (p) is greater than the number of observations (n) (p>n). Which is the best technique to use and why ?

Question Posted / Surya Prakash

1 Answers
67 Views
I also Faced
E-Mail Answers

Answer Posted / Surya Prakash

When dealing with high-dimensional data (p > n), techniques like regularization, feature selection, and dimensionality reduction become important. Regularization helps prevent overfitting by adding a penalty term to the loss function, feature selection helps select only the most relevant features, and dimensionality reduction techniques like PCA or t-SNE help reduce the number of dimensions while retaining as much information as possible.

Is This Answer Correct ?

0 Yes

0 No

Post New Answer View All Answers

Please Help Members By Posting Answers For Below Questions

Tell me what are the last machine learning papers you've read?

208

Do you have research experience in machine learning?

What are standardization and normalisation? Give one advantage of each over the other?

Tell us do you have research experience in machine learning?

149