Week 45, Recurrent Neural Networks

 

 

 

Other ways of presenting a classification problem

For a binary classifcation matrix, the so-called confusion matrix, is often used. It can also be extended to more catgeories/classes as well. The following quantities are then used

  1. positive condition number \( P \), which represents the number of real positive cases in the data (output one/true etc)
  2. The condition negative number \( N \) which is the number of negative cases (ouput zero/false etc)
  3. The true positive number \( TP \) which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)
  4. The true negative \( TN \) number which represents whether a negative test has been correctly classified
  5. The false positive \( FP \) number, a so-called type I error which tells us about the fraction of positive test result which are wrongly classified
  6. A false negative \( FN \) number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.

It is is easy to think in terms of illness. You could think of the above as

  1. True positive: Sick people correctly identified as sick
  2. False positive: Healthy people incorrectly identified as sick
  3. True negative: Healthy people correctly identified as healthy
  4. False negative: Sick people incorrectly identified as healthy