title: How specific should a Support Vector Machine Model be?
body: <p>The whole point of using an SVM is that the algorithm will be able to decide whether an input is true or false etc etc.</p>
  
  <p>I am trying to use an SVM for predictive maintenance to predict how likely a system is to overheat.</p>
  
  <p>For my example, the range is 0-102°C and if the temperature reaches 80°C or above it's classed as a failure.</p>
  
  <p>My inputs are arrays of 30 doubles(the last 30 readings).</p>
  
  <p>I am making some sample inputs to train the SVM and I was wondering if it is good practice to pass in very specific data to train it - eg passing in arrays 80°C, 81°C ... 102°C so that the model will automatically associate these values with failure. You could do an array of 30 x 79°C as well and set that to pass.</p>
  
  <p>This seems like a complete way of doing it, although if you input arrays like that - would it not be the same as hardcoding a switch statement to trigger when the temperature reads 80->102°C.</p>
  
  <p>Would it be a good idea to pass in these "hardcoded" style arrays or should I stick to more random inputs?</p>