What is Cross Validation?

What is Cross Validation?

Cross Validation is a technique which involves reserving a particular sample of a dataset on which you do not train the model. Later, you test your model on this sample before finalizing it.
Here are the steps involved in cross validation:
  1. You reserve a sample data set
  2. Train the model using the remaining part of the dataset
  3. Use the reserve sample of the test (validation) set. This will help you in gauging the effectiveness of your model’s performance. If your model delivers a positive result on validation data, go ahead with the current model. It rocks!

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