Advantage and Disadvantages of Supervised learning?

Advantages of Supervised learning:

  • With the help of supervised learning, the model can predict the output on the basis of prior experiences.
  • In supervised learning, we can have an exact idea about the classes of objects.
  • Supervised learning model helps us to solve various real-world problems such as fraud detection, spam filtering , etc.

Disadvantages of supervised learning:

  • Supervised learning models are not suitable for handling the complex tasks.
  • Supervised learning cannot predict the correct output if the test data is different from the training dataset.
  • Training required lots of computation times.
  • In supervised learning, we need enough knowledge about the classes of object.


  1. You can get very specific about the definition of the classes, which means that you can train the classifier in a way which has a perfect decision boundary to distinguish different classes accurately.

  2. You can specifically determine how many classes you want to have.

  3. After training, you don’t necessarily need to keep the training examples in a memory. You can keep the decision boundary as a mathematical formula and that would be enough for classifying future inputs.


  1. Your decision boundary might be overtrained. Which means that if your training set is not including some examples that you want to have in a class, when you use those examples after training, you might not get the correct class label.

  2. When this an input which is not from any of the classes in reality, then it might get a wrong class label after classification.

  3. You have to select a lot of good examples from each class while you are training the classifier. If you consider classification of big data that can be a real challenge.

  4. Training needs a lot of computation time, so do the classification.

  5. You might need to use a cloud and leave the training algorithm work over a night or nights before obtaining a good decision boundary model.