Hello,
I've ran these steps and I have some questions, I'd be glad to find the answer for.
1 What are the eigen values? how are they calculated and used in this algorithm?
2 What is the difference between the cross validation accuracy and the training set accuracy? 2.1 which one do I choose to describe the accuracy of the classifier? 2.2 I've noticed that the relation between cross validation accuracy and the training set accuracy is an inverse relation, such that if the lower the cross validation accuracy the higher the training set accuracy, why?
3 I didn't fully understand this, can someone explain?
<Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Cls vs cls 1 2 <Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Target 1: 51.2 48.8 %, 240 examples <Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Target 2: 1.5 98.5 %, 1200 examples <Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Training set accuracy is 93.3333% (optimistic) <Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Cls vs cls 1 2 <Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Target 1: 61.3 38.8 %, 240 examples <Box algorithm::(0x0a5a6a4a, 0x1d92a778) aka Classifier trainer> Target 2: 0.3 99.8 %, 1200 examples
Thank you for any help you might provide.
