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Steps which are involved in creating the classifier system

 

Following are the steps which are involved in creating the classifier system 

 

  1. Collect some Quality Data (Also called training data): As you are going to train your system, hence you need some quality dataset, which you will to create better model.

     

  2. Feature selection : Now you have to select the right features to do better classifications, example of features are

     

    1. In case of email : It would be word

    2. In case of weather dataset : It could be temperature

    3. In case of Movies : It could be genre

       

  3.  Now prepare your training data: Now fetch the features from the training datasets in the form, which we will with the algorithm we chose for the model.

     

  4. Training Dataset is ready, run the algorithms: Build the classifier using our training data we have created in previous steps. And you must try multiple algorithms so that you will get to know which one will perform better compare to others.

     

  5. Testing: Test the output or validate it weather it performed correctly or not.

     

 

Training: So the training is a process, using the datasets we create the model, and this model will be used by the classifier to categorize unseen documents. 

 

Classifier: A classifier uses a model created by training data to make the predictions about unseen documents and datasets.

 

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