Best Answer

• Options

  varunm1 Moderator, Member Posts: 1,207 Unicorn

  September 2019 edited September 2019 Solution Accepted

  Hello @rresetar
  
  Based on the image you posted. The overall accuracy of your algorithm is 74.1 %.
  
  The table shown is a confusion matrix, this table helps understand the correct and incorrect predictions made by your algorithm.
  
  For example, The first column is related to the original label "neutral" in your data set. There are a total of (638+66+16 = 720) samples that are labeled neutral in your original data set. Now we compare that with all rows in the first column to see how they are classified by the algorithm. From this column, it is evident that the algorithm classified 638 samples as neutral (correct), 66 as positive (Wrong) and 16 as negative (wrong) that belongs to 720 samples of neutral. Now class recall for neutral class is 638/(638+66+16) *100. This means (Total number of samples predicted correctly in a specific class)/(Total samples in data that belong to that class). This is similar to the other two rows Class recall for positive is 75/(75+111) and negative is 28/(63+3+28). 
  
  If you look at the other end there is a column for class precision, this is calculated based on (number samples of a class that are predicted correctly/ total number of samples predicted as that class). For example, let's take neutral class, the class precision for neutral class is the (638/(638+111+63)), this means that 638 predictions belong to neutral class and the rest 111 and 63 that were predicted as neutral class belongs to positive and negative classes, similarly for other classes as well.
  
  You can explain, the accuracy, individual class precision, and recall.
  
  To understand in-depth you can look at this wiki
  https://en.wikipedia.org/wiki/Precision_and_recall
  
  Hope this helps. Please inform if you need more information.

  Regards,
  Varun
  https://www.varunmandalapu.com/
  
  

  Be Safe. Follow precautions and Maintain Social Distancing

  8