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How to prevent overfitting and underfitting

       OVERFITTING AND UNDERFITTING

Overfitting

  • It will perform well on its training data, poorly on new unseen data.

    Handling Overfitting : 

    • Cross-validation 
      • This is done by splitting your dataset into ‘test’ data and ‘train’ data. Build the model using the ‘train’ set. The ‘test’ set is used for in-time validation. This way you know what the expected output is and you will easily be able to judge the accuracy of your model.
    • Regularization 
      • This is a form of regression, that regularizes or shrinks the coefficient estimates towards zero. This technique discourages learning a more complex model.
    • Early stopping 
      • When training a learner with an iterative method, you stop the training process before the final iteration. This prevents the model from memorizing the dataset.
    • Pruning 
      • This technique applies to decision trees. 
      • Pre-pruning: Stop ‘growing’ the tree earlier before it perfectly classifies the training set. 
      • Post-pruning: Allows the tree to ‘grow’, perfectly classify the training set and then post prune the tree. 
    • Dropout 
      • This is a technique where randomly selected neurons are ignored during training
    • Regularize the weights. 
    • Removing Irrelevant input features.
    • Removing outliers or anomalies.

Underfitting

  • Underfitting is a modeling error which occurs when a function does not fit the data points well enough. It is the result of a simple model with an insufficient number of training points. A model that is under fitted is inaccurate because the trend does not reflect the reality of the data. 

    Handling Underfitting : 

  • Get more training data. 
  • Increase the size or number of parameters in the model. 
  • Increase the complexity of the model. 
  • Increasing the training time, until cost function is minimized. 
  • With these techniques, you should be able to improve your models and correct any overfitting or underfitting issues.        





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