"No one is harder on a talented person than the person themselves" - Linda Wilkinson ; "Trust your guts and don't follow the herd" ; "Validate direction not destination" ;

November 11, 2016

Day #41 - Machine Learning Interesting Qns

I do read through a lot of materials. Some readings are very clear and needs bookmark. Some of those questions and answers
  1. How does KNN predict class label for new example ?
    • Find the nearest K neighbour of example which needs to be classified. Take the major vote based on class labels of the K neighbours found
  2. Classification - Map input to discrete outputs
  3. Generative Model - Naive Bayes
  4. Discriminative Model - SVM, Decision Trees, Neural Networks, Boosting, KNN
  5. Regression - Map input to continuous outputs
  6. Decision Tress - Embedded Implicit Feature Selection method
  7. PCA
    • Taking Data into a new space
    • Number of Eigen Values = Number of original dimensions
    • Pick the top k Eigen Value Vectors

       8. Linearly non-separable in normal plane. With SVM Kernal Technique we can project it in hyper plane and make it linearly separable

       9. Linearly Separable

Happy Learning!!!
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