title: Prediction using RandomForest Algorithm, Sensitivity and Specificity
body: <p>I'm new to ML and developing a simple Prediction model using Random Forest algorithm. I'm also evaluating my model using Confusion Matrix and computing the Sensitivity and Specificity scores of my built model.</p>
  
  <p>While I conceptually understand these two, I'm now stuck at a point where my model always results in the sensitivity score of 1 and specificity score of 0. It gets an accuracy score of 91% (91/100 training samples are predicted correctly).</p>
  
  <p>Now what do I infer from this? Is my model too sensitive and not specific? Will I have to understand that my model will be more bound to emit False Positives and never False Negatives? (This may be good in my scenario) as the domain I'm dealing with is predict frauds where false positives are better.</p>
  
  <p>As a side note, on a typical ML workflow, how the Sensitivity and Specificity scores are often used in practise? Is it used to keep some sort of a threshold on the client systems to take the prediction with a pinch of salt? What would be the parameters I can tune to get different values for these? At the moment, I just tweaked the Random Forest model variables like no of trees, no of nodes etc. But these numbers always remain the same.</p>